CN114697543B

CN114697543B - Image reconstruction method, related device and system

Info

Publication number: CN114697543B
Application number: CN202011639566.0A
Authority: CN
Inventors: 吕飞帆; 张运超
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2023-05-19
Anticipated expiration: 2040-12-31
Also published as: WO2022143921A1; CN114697543A

Abstract

The application discloses an image reconstruction method, wherein an electronic device can extract image features of specified target content from a shot low-definition image, and match high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library of the same class as the specified target content on a cloud server. The electronic device may fuse the high definition texture feature into a low definition image to obtain a high definition image. Therefore, the super-division reconstruction can be performed aiming at the specific target in a targeted manner, and the operand on the electronic equipment in the super-division reconstruction process is simplified.

Description

Image reconstruction method, related device and system

Technical Field

The present disclosure relates to the field of computer vision, and in particular, to a method, an apparatus and a system for image reconstruction.

Background

Smartphones have evolved to date and photographing has become one of its most important features. The basic imaging device and the imaging algorithm are greatly developed and changed, the mobile phone photographing transformation is promoted again and again, and the photographing experience of a user is improved. However, following this further revolution, the cost of the device is also increasing and the imaging quality is approaching the bottleneck. The shooting of remote scenes is an important component of mobile phone shooting, and the quality of the telephoto quality seriously affects the evaluation of the shooting level of the mobile phone by a user.

In order to obtain higher-quality telephoto quality, at present, electronic devices such as mobile phones and the like mostly adopt a means of combining optical zooming and digital zooming to improve the telephoto imaging quality. For the digital zoom phase, image reconstruction methods based on deep learning are typically employed on electronic devices. For example, a large convolutional neural network (convolutional neural networks, CNN) is trained using a large number of low-definition and high-definition image samples of different shot scenes, and the CNN model can be used to perform super-resolution reconstruction processing on any class of input images. In order to ensure the effect of super-resolution reconstruction, the CNN model needs to consider a plurality of scenes, but the CNN model is generally large, and when the CNN model is operated on electronic equipment such as a mobile phone, the risk of insufficient performance possibly exists, and high-quality pictures are difficult to quickly super-resolution reconstruct after photographing.

Disclosure of Invention

The application provides an image reconstruction method, a related device and a system, which realize that specific target content is identified and cut out from a shot photo, and then the cut-out image is subjected to super-resolution reconstruction processing to obtain a high-definition image, so that the imaging quality of the specific target in the shot photo is improved.

In a first aspect, the present application provides an image reconstruction method, including: the electronic device receives a first input; the electronic equipment responds to the first input to acquire a low-definition image acquired by the camera; the electronic equipment detects that the low-definition image comprises first specified target content, and the category of the first specified target content is a first category; the electronic equipment extracts first image characteristics of the first appointed target content in the low-definition image; the electronic device sends the first image feature and the first category to a cloud server; the cloud server is used for matching a first high-definition texture dictionary library with a shooting target class as a first class from a plurality of stored high-definition texture dictionary libraries with different shooting target classes; the cloud server is used for matching first high-definition texture features, the similarity of which with the first image features is larger than a preset value, from the first high-definition texture dictionary library; the electronic equipment receives the first high-definition texture feature sent by the cloud server; the electronic device fuses the first high-definition texture feature into the low-definition image to obtain a high-definition image, wherein the resolution of the first specified target content in the high-definition image is larger than that of the first specified target content in the low-definition image.

According to the image reconstruction method, the electronic equipment can extract the image features of the appointed target content from the shot low-definition image, and the high-definition texture features similar to the image features of the appointed target content are matched based on the high-definition texture dictionary library of the same category as the appointed target content on the cloud server. The electronic device may fuse the high definition texture feature into a low definition image to obtain a high definition image. Therefore, the super-division reconstruction can be performed aiming at the specific target in a targeted manner, and the operand on the electronic equipment in the super-division reconstruction process is simplified.

In one possible implementation manner, the electronic device extracts a first image feature of the first specified target content in the low-definition image, specifically including: the electronic equipment identifies and cuts out a first area where the first appointed target content is located in the low-definition image to obtain a first low-definition cut-out image, wherein the first low-definition cut-out image comprises the first appointed target content; the electronic device extracts a first image feature of the first low definition cropped image.

In one possible implementation manner, the electronic device fuses the first high-definition texture feature into the low-definition image to obtain a high-definition image, which specifically includes: the electronic equipment fuses the first high-definition texture features into the first low-definition clipping image to obtain a first high-definition clipping image; the electronic device replaces the first high-definition clipping image with the first low-definition clipping image, and pastes the first high-definition clipping image back to the first area in the low-definition image to obtain the high-definition image.

In one possible implementation, after the electronic device fuses the first high-definition texture feature into the low-definition image to obtain a high-definition image, the method further includes: the electronic device saves the high definition image.

In one possible implementation, before the electronic device receives the first input, the method further includes: the electronic equipment displays a shooting preview interface, wherein a shooting key and a preview image stream acquired by a camera in real time are displayed on the shooting preview interface, and the first input is input aiming at the shooting key.

In one possible implementation, the shooting preview interface further displays a first control; before the electronic device receives the first input, the method further comprises: the electronic device receives a second input for the first control; in response to the second input, the electronic device turns on a super-resolution reconstruction mode.

In one possible implementation, before the electronic device receives the first input, the method further includes: the electronic device displays a picture browsing interface, the picture browsing interface is displayed with the low-definition image and a second control, and the first input is input aiming at the second control.

In one possible implementation, the preview image stream includes a first preview image; before the electronic device receives the first input, the method further comprises: the electronic equipment detects that the first preview image comprises second specified target content, wherein the category of the second specified target content is a second category which is the same as or different from the first category; the electronic device extracts second image features in the first preview image; the electronic device sends the second image feature and the second category to a cloud server; the cloud server is used for matching a second high-definition texture dictionary library with a shooting target class as a second class from the stored high-definition texture dictionary libraries with a plurality of different shooting target classes; the cloud server is used for matching second high-definition texture features, the similarity of which with the second image features is larger than a preset value, from the second high-definition texture dictionary library; the electronic equipment receives the second high-definition texture feature sent by the cloud server; the electronic device stores the second high definition texture feature in a cache queue.

In one possible implementation manner, the electronic device extracts the second image feature of the second specified target content in the first preview image, and specifically includes: the electronic equipment identifies and cuts out a second area where the second specified target content is located in the low-definition image to obtain a second low-definition cut image; the electronic device extracts a second image feature of the second low definition cropped image.

In one possible implementation, after the electronic device detects that the first preview image includes the second specified target content, the method further includes: the electronic device displays category information on the shooting preview interface, wherein the category information is used for indicating that the category of the appointed target content in the preview image stream is the second category.

In one possible implementation, after the electronic device stores the second high-definition texture feature in a cache queue, the method further includes; the electronic equipment acquires a second preview image acquired by the camera; the electronic equipment detects that the second preview image comprises third specified target content, wherein the class of the third specified target content is a third class which is the same as or different from the second class; the electronic equipment identifies and cuts out a third area where the third appointed target content is located in the low-definition image to obtain a third low-definition cut image; the electronic device extracting a third image feature of the third low definition cropped image; the electronic equipment judges whether high-definition texture features with similarity with the third image features larger than a preset value are stored in the cache queue; if the cache queue stores high-definition texture features with the similarity to the third image features being larger than a preset value, the electronic device fuses the high-definition texture features with the similarity to the third image features being larger than the preset value in the cache queue into the third low-definition clipping image to obtain a third high-definition clipping image; the electronic equipment replaces the third high-definition clipping image with the third low-definition clipping image, and pastes the third high-definition clipping image back to a third area in the second preview image to obtain a high-definition preview image; the electronic device displays the high-definition preview image on the capture preview interface.

In one possible implementation manner, if the cache queue does not store high-definition texture features having a similarity with the third image feature greater than a preset value, the method further includes: the electronic device sends the third image feature and the third category to a cloud server; the cloud server is used for matching a third high-definition texture dictionary library with a shooting target class as a third class from a plurality of stored high-definition texture dictionary libraries with different shooting target classes; the cloud server is used for matching a third high-definition texture feature with the similarity larger than a preset value with the third image feature from the third high-definition texture dictionary library; the electronic equipment receives the third high-definition texture feature sent by the cloud server; and the electronic equipment stores the third high-definition texture feature into the cache queue, and fuses the third high-definition texture feature into the third low-definition clipping image to obtain the third high-definition clipping image.

In one possible implementation, before the electronic device sends the first image feature and the first category to a cloud server, the method further includes: the electronic equipment judges whether high-definition texture features with similarity larger than a preset value with the first image features are stored in the cache queue or not; the electronic device sends the first image feature and the first category to a cloud server, and specifically includes: and if the cache queue does not store the high-definition texture features with the similarity to the first image features being larger than a preset value, the electronic equipment sends the first image features and the first types to the cloud server.

In one possible implementation manner, if the cache queue stores high-definition texture features having a similarity with the first image feature greater than a preset value, the method further includes: and the electronic equipment fuses the high-definition texture features, which have the similarity with the first image features larger than a preset value, in the cache queue into the low-definition image to obtain the high-definition image.

In a second aspect, the present application provides an electronic device comprising one or more processors and one or more memories. The one or more memories are coupled to the one or more processors, the one or more memories being operable to store computer program code comprising computer instructions that, when executed by the one or more processors, cause the electronic device to perform the image reconstruction method in any of the possible implementations of the above aspect.

In a third aspect, embodiments of the present application provide a computer storage medium comprising computer instructions that, when executed on an electronic device, cause the electronic device to perform the image reconstruction method in any one of the possible implementations of the above aspect.

In a fourth aspect, embodiments of the present application provide a computer program product, which when run on a computer causes the computer to perform the image reconstruction method in any one of the possible implementations of the above aspect.

In a fifth aspect, an embodiment of the present application provides an image reconstruction system, including an electronic device and a cloud server, where the cloud server stores a plurality of high-definition texture dictionary libraries of different shooting target classes; wherein the electronic device is configured to receive a first input; the electronic equipment is also used for responding to the first input and acquiring a low-definition image acquired by the camera; the electronic device is further configured to detect that the low-definition image includes a first specified target content, where a category of the first specified target content is a first category; the electronic equipment is also used for extracting first image features of the first appointed target content in the low-definition image; the electronic device is further configured to send the first image feature and the first category to the cloud server; the cloud server is used for matching a first high-definition texture dictionary library with the shooting target class as a first class from the stored high-definition texture dictionary libraries with the different shooting target classes; the cloud server is further used for matching first high-definition texture features, the similarity of which with the first image features is larger than a preset value, from the first high-definition texture dictionary library; the cloud server is further configured to send the first high-definition texture feature to the electronic device; the electronic device is further configured to fuse the first high-definition texture feature into the low-definition image to obtain a high-definition image, where a resolution of the first specified target content in the high-definition image is greater than a resolution of the first specified target content in the low-definition image.

In one possible implementation, the cloud server is further configured to acquire a plurality of high-quality images and a shooting target class of each high-quality image; the cloud server is also used for extracting high-definition texture features of shooting target categories in the high-quality images and storing the high-definition texture features in a high-definition texture dictionary library corresponding to the shooting target categories of the high-quality images.

In one possible implementation, the electronic device is further configured to send the set of identification categories of the object identification model on the electronic device to the cloud server; the cloud server is further configured to send model update information to the electronic device when it is determined that the identification class set is different from class sets of the high-definition texture dictionary libraries of the plurality of different shooting target classes; the electronic equipment is also used for updating the target recognition model on the electronic equipment based on the model updating information; after updating, the identification class set of the target identification model on the electronic equipment is the same as the class set of the high-definition texture dictionary library of the plurality of different shooting target classes.

Wherein the electronic device is further configured to perform the image reconstruction method in any of the possible implementations of the above aspect.

In a sixth aspect, embodiments of the present application provide a chip system, including: one or more processors and one or more memories; the one or more memories are configured to store computer program code comprising computer instructions that, when executed by the one or more processors, cause the image reconstruction system to perform the image reconstruction method in any of the possible implementations of the above aspects.

The one or more processors may include one or more of an application processor, an image signal processor, a digital signal processor, a neural network processor, a graphics processor, among others.

Drawings

Fig. 1 is a schematic structural diagram of a communication system according to an embodiment of the present application;

fig. 2A is a schematic hardware structure of an electronic device according to an embodiment of the present application;

fig. 2B is a schematic software structure of an electronic device according to an embodiment of the present application;

fig. 2C is a schematic hardware structure of a cloud server according to an embodiment of the present application;

FIGS. 3A-3E are a set of interface diagrams provided in embodiments of the present application;

FIGS. 4A-4B are a schematic diagram of another set of interfaces provided in an embodiment of the present application;

FIGS. 5A-5B are a schematic diagram of another set of interfaces provided in an embodiment of the present application;

FIGS. 6A-6E are another set of interface schematic diagrams provided by embodiments of the present application;

fig. 7 is a schematic flow chart of an image reconstruction method according to an embodiment of the present application;

FIG. 8A is a schematic diagram of a low-definition image according to an embodiment of the present disclosure;

FIG. 8B is a schematic diagram of a specified target content mask (mask) image according to an embodiment of the present application;

fig. 8C is a schematic diagram of a crop box in a low-definition image according to an embodiment of the present application;

FIG. 8D is a schematic diagram of a first low definition cropped image according to an embodiment of the present application;

fig. 8E is a schematic diagram of a first high definition clipping image according to an embodiment of the present application;

fig. 8F is a schematic diagram of a high-definition image according to an embodiment of the present disclosure;

fig. 9 is a flowchart of an image reconstruction method according to another embodiment of the present application;

fig. 10 is a flowchart of an image reconstruction method according to another embodiment of the present application;

fig. 11 is a flowchart of an image reconstruction method according to another embodiment of the present application;

fig. 12 is a schematic structural diagram of an image reconstruction system according to an embodiment of the present application.

Detailed Description

The following description will be given in detail of the technical solutions in the embodiments of the present application with reference to the accompanying drawings. Wherein, in the description of the embodiments of the present application, "/" means or is meant unless otherwise indicated, for example, a/B may represent a or B; the text "and/or" is merely an association relation describing the associated object, and indicates that three relations may exist, for example, a and/or B may indicate: the three cases where a exists alone, a and B exist together, and B exists alone, and in addition, in the description of the embodiments of the present application, "plural" means two or more than two.

The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.

The embodiment of the application provides an image reconstruction method, wherein an electronic device can extract image features of specified target content from a shot low-definition image, and match high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library of the same class as the specified target content on a cloud server. The electronic device may fuse the high definition texture feature into a low definition image to obtain a high definition image. Therefore, the super-division reconstruction can be performed aiming at the specific target in a targeted manner, and the operand on the electronic equipment in the super-division reconstruction process is simplified.

A communication system 10 according to an embodiment of the present application is described below.

Referring to fig. 1, fig. 1 is a schematic diagram of a communication system 10 according to an embodiment of the application. The communication system 10 may include a terminal 100 and a cloud server 200. The terminal 100 may be a mobile phone, a tablet computer, a desktop computer, a laptop computer, a handheld computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a cellular phone, a personal digital assistant (personal digital assistant, PDA), an augmented reality (augmented reality, AR), a Virtual Reality (VR) device, or the like. The embodiment of the present application does not particularly limit the specific type of the terminal 100.

The terminal 100 may connect to the cloud server 200 through a 2G network, a 3G network, a 4G network, a 5G network, a wireless local area network (wireless local area network, WLAN), or the like. Wherein the terminal 100 may transmit the image feature/image data and category information specifying the target content in the image feature/image data to the cloud server 200. The cloud server 200 may transmit the high definition texture features and the like to the terminal 100.

The cloud server 200 may establish connection with a plurality of terminals 100, and may independently process processing tasks requested by the plurality of terminals 100. The cloud server 200 may distinguish the terminals through an account (for example, a Hua-as account) on the terminals where the user logs in.

Fig. 2A shows a schematic structural diagram of the electronic device 100.

The embodiment will be specifically described below taking the electronic device 100 as an example. It should be understood that the electronic device 100 shown in fig. 2A is only one example, and that the electronic device 100 may have more or fewer components than shown in fig. 2A, may combine two or more components, or may have a different configuration of components. The various components shown in fig. 2A may be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and/or application specific integrated circuits.

The electronic device 100 may include: processor 110, external memory interface 120, internal memory 121, universal serial bus (universal serial bus, USB) interface 130, charge management module 140, power management module 141, battery 142, antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headset interface 170D, sensor module 180, keys 190, motor 191, indicator 192, camera 193, display 194, and subscriber identity module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyro sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, and the like.

It should be understood that the illustrated structure of the embodiment of the present invention does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, electronic device 100 may include more or fewer components than shown, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units, such as: the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a memory, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor (neural-network processing unit, NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.

The controller may be a neural hub and a command center of the electronic device 100, among others. The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. Repeated accesses are avoided and the latency of the processor 110 is reduced, thereby improving the efficiency of the system.

In some embodiments, the processor 110 may include one or more interfaces. The interfaces may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous receiver transmitter (universal asynchronous receiver/transmitter, UART) interface, a mobile industry processor interface (mobile industry processor interface, MIPI), a general-purpose input/output (GPIO) interface, a subscriber identity module (subscriber identity module, SIM) interface, and/or a universal serial bus (universal serial bus, USB) interface, among others.

The I2C interface is a bi-directional synchronous serial bus comprising a serial data line (SDA) and a serial clock line (derail clock line, SCL). In some embodiments, the processor 110 may contain multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, charger, flash, camera 193, etc., respectively, through different I2C bus interfaces. For example: the processor 110 may be coupled to the touch sensor 180K through an I2C interface, such that the processor 110 communicates with the touch sensor 180K through an I2C bus interface to implement a touch function of the electronic device 100.

The I2S interface may be used for audio communication. In some embodiments, the processor 110 may contain multiple sets of I2S buses. The processor 110 may be coupled to the audio module 170 via an I2S bus to enable communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may transmit an audio signal to the wireless communication module 160 through the I2S interface, to implement a function of answering a call through the bluetooth headset.

PCM interfaces may also be used for audio communication to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface to implement a function of answering a call through the bluetooth headset. Both the I2S interface and the PCM interface may be used for audio communication.

The UART interface is a universal serial data bus for asynchronous communications. The bus may be a bi-directional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is typically used to connect the processor 110 with the wireless communication module 160. For example: the processor 110 communicates with a bluetooth module in the wireless communication module 160 through a UART interface to implement a bluetooth function. In some embodiments, the audio module 170 may transmit an audio signal to the wireless communication module 160 through a UART interface, to implement a function of playing music through a bluetooth headset.

The MIPI interface may be used to connect the processor 110 to peripheral devices such as a display 194, a camera 193, and the like. The MIPI interfaces include camera serial interfaces (camera serial interface, CSI), display serial interfaces (display serial interface, DSI), and the like. In some embodiments, processor 110 and camera 193 communicate through a CSI interface to implement the photographing functions of electronic device 100. The processor 110 and the display 194 communicate via a DSI interface to implement the display functionality of the electronic device 100.

The GPIO interface may be configured by software. The GPIO interface may be configured as a control signal or as a data signal. In some embodiments, a GPIO interface may be used to connect the processor 110 with the camera 193, the display 194, the wireless communication module 160, the audio module 170, the sensor module 180, and the like. The GPIO interface may also be configured as an I2C interface, an I2S interface, a UART interface, an MIPI interface, etc.

The USB interface 130 is an interface conforming to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 130 may be used to connect a charger to charge the electronic device 100, and may also be used to transfer data between the electronic device 100 and a peripheral device. And can also be used for connecting with a headset, and playing audio through the headset. The interface may also be used to connect other electronic devices, such as AR devices, etc.

It should be understood that the interfacing relationship between the modules illustrated in the embodiments of the present invention is only illustrative, and is not meant to limit the structure of the electronic device 100. In other embodiments of the present application, the electronic device 100 may also use different interfacing manners, or a combination of multiple interfacing manners in the foregoing embodiments.

The charge management module 140 is configured to receive a charge input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charge management module 140 may receive a charging input of a wired charger through the USB interface 130. In some wireless charging embodiments, the charge management module 140 may receive wireless charging input through a wireless charging coil of the electronic device 100. The charging management module 140 may also supply power to the electronic device through the power management module 141 while charging the battery 142.

The power management module 141 is used for connecting the battery 142, and the charge management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140 and provides power to the processor 110, the internal memory 121, the external memory, the display 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be configured to monitor battery capacity, battery cycle number, battery health (leakage, impedance) and other parameters. In other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charge management module 140 may be disposed in the same device.

The wireless communication function of the electronic device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, a modem processor, a baseband processor, and the like.

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the electronic device 100 may be used to cover a single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed into a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide a solution for wireless communication including 2G/3G/4G/5G, etc., applied to the electronic device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA), etc. The mobile communication module 150 may receive electromagnetic waves from the antenna 1, perform processes such as filtering, amplifying, and the like on the received electromagnetic waves, and transmit the processed electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can amplify the signal modulated by the modem processor, and convert the signal into electromagnetic waves through the antenna 1 to radiate. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be provided in the same device as at least some of the modules of the processor 110.

The modem processor may include a modulator and a demodulator. The modulator is used for modulating the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low frequency baseband signal to the baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred to the application processor. The application processor outputs sound signals through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays images or video through the display screen 194. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be provided in the same device as the mobile communication module 150 or other functional module, independent of the processor 110.

The wireless communication module 160 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field wireless communication technology (near field communication, NFC), infrared technology (IR), etc., as applied to the electronic device 100. The wireless communication module 160 may be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the electromagnetic wave signals, filters the electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be transmitted from the processor 110, frequency modulate it, amplify it, and convert it to electromagnetic waves for radiation via the antenna 2.

In some embodiments, antenna 1 and mobile communication module 150 of electronic device 100 are coupled, and antenna 2 and wireless communication module 160 are coupled, such that electronic device 100 may communicate with a network and other devices through wireless communication techniques. The wireless communication techniques may include the Global System for Mobile communications (global system for mobile communications, GSM), general packet radio service (general packet radio service, GPRS), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (wideband code division multiple access, WCDMA), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (long term evolution, LTE), BT, GNSS, WLAN, NFC, FM, and/or IR techniques, among others. The GNSS may include a global satellite positioning system (global positioning system, GPS), a global navigation satellite system (global navigation satellite system, GLONASS), a beidou satellite navigation system (beidou navigation satellite system, BDS), a quasi zenith satellite system (quasi-zenith satellite system, QZSS) and/or a satellite based augmentation system (satellite based augmentation systems, SBAS).

The electronic device 100 implements display functions through a GPU, a display screen 194, an application processor, and the like. The GPU is a microprocessor for image processing, and is connected to the display 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, and the like. The display 194 includes a display panel. The display panel may employ a liquid crystal display (liquid crystal display, LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED) or an active-matrix organic light-emitting diode (matrix organic light emitting diode), a flexible light-emitting diode (flex), a mini, a Micro led, a Micro-OLED, a quantum dot light-emitting diode (quantum dot light emitting diodes, QLED), or the like. In some embodiments, the electronic device 100 may include 1 or N display screens 194, N being a positive integer greater than 1.

The electronic device 100 may implement photographing functions through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.

The ISP is used to process data fed back by the camera 193. For example, when photographing, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electric signal, and the camera photosensitive element transmits the electric signal to the ISP for processing and is converted into an image visible to naked eyes. ISP can also optimize the noise, brightness and skin color of the image. The ISP can also optimize parameters such as exposure, color temperature and the like of a shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image onto the photosensitive element. The photosensitive element may be a charge coupled device (charge coupled device, CCD) or a Complementary Metal Oxide Semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, which is then transferred to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV, or the like format. In some embodiments, electronic device 100 may include 1 or N cameras 193, N being a positive integer greater than 1.

The digital signal processor is used for processing digital signals, and can process other digital signals besides digital image signals. For example, when the electronic device 100 selects a frequency bin, the digital signal processor is used to fourier transform the frequency bin energy, or the like.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 may play or record video in a variety of encoding formats, such as: dynamic picture experts group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

The NPU is a neural-network (NN) computing processor, and can rapidly process input information by referencing a biological neural network structure, for example, referencing a transmission mode between human brain neurons, and can also continuously perform self-learning. Applications such as intelligent awareness of the electronic device 100 may be implemented through the NPU, for example: image recognition, face recognition, speech recognition, text understanding, etc.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to enable expansion of the memory capabilities of the electronic device 100. The external memory card communicates with the processor 110 through an external memory interface 120 to implement data storage functions. For example, files such as music, video, etc. are stored in an external memory card.

The internal memory 121 may be used to store computer executable program code including instructions. The processor 110 executes various functional applications of the electronic device 100 and data processing by executing instructions stored in the internal memory 121. The internal memory 121 may include a storage program area and a storage data area. The storage program area may store an application program (such as a sound playing function, an image playing function, etc.) required for at least one function of the operating system, etc. The storage data area may store data created during use of the electronic device 100 (e.g., audio data, phonebook, etc.), and so on. In addition, the internal memory 121 may include a high-speed random access memory, and may further include a nonvolatile memory such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (universal flash storage, UFS), and the like.

The electronic device 100 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, an application processor, and the like. Such as music playing, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal output and also to convert an analog audio input into a digital audio signal. The audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be disposed in the processor 110, or a portion of the functional modules of the audio module 170 may be disposed in the processor 110.

The speaker 170A, also referred to as a "horn," is used to convert audio electrical signals into sound signals. The electronic device 100 may listen to music, or to hands-free conversations, through the speaker 170A.

A receiver 170B, also referred to as a "earpiece", is used to convert the audio electrical signal into a sound signal. When electronic device 100 is answering a telephone call or voice message, voice may be received by placing receiver 170B in close proximity to the human ear.

Microphone 170C, also referred to as a "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or transmitting voice information, the user can sound near the microphone 170C through the mouth, inputting a sound signal to the microphone 170C. The electronic device 100 may be provided with at least one microphone 170C. In other embodiments, the electronic device 100 may be provided with two microphones 170C, and may implement a noise reduction function in addition to collecting sound signals. In other embodiments, the electronic device 100 may also be provided with three, four, or more microphones 170C to enable collection of sound signals, noise reduction, identification of sound sources, directional recording functions, etc.

The earphone interface 170D is used to connect a wired earphone. The headset interface 170D may be a USB interface 130 or a 3.5mm open mobile electronic device platform (open mobile terminal platform, OMTP) standard interface, a american cellular telecommunications industry association (cellular telecommunications industry association of the USA, CTIA) standard interface.

The pressure sensor 180A is used to sense a pressure signal, and may convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194. The pressure sensor 180A is of various types, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a capacitive pressure sensor comprising at least two parallel plates with conductive material. The capacitance between the electrodes changes when a force is applied to the pressure sensor 180A. The electronic device 100 determines the strength of the pressure from the change in capacitance. When a touch operation is applied to the display screen 194, the electronic apparatus 100 detects the touch operation intensity according to the pressure sensor 180A. The electronic device 100 may also calculate the location of the touch based on the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch location, but at different touch operation strengths, may correspond to different operation instructions. For example: and executing an instruction for checking the short message when the touch operation with the touch operation intensity smaller than the first pressure threshold acts on the short message application icon. And executing an instruction for newly creating the short message when the touch operation with the touch operation intensity being greater than or equal to the first pressure threshold acts on the short message application icon.

The gyro sensor 180B may be used to determine a motion gesture of the electronic device 100. In some embodiments, the angular velocity of electronic device 100 about three axes (i.e., x, y, and z axes) may be determined by gyro sensor 180B. The gyro sensor 180B may be used for photographing anti-shake. For example, when the shutter is pressed, the gyro sensor 180B detects the shake angle of the electronic device 100, calculates the distance to be compensated by the lens module according to the angle, and makes the lens counteract the shake of the electronic device 100 through the reverse motion, so as to realize anti-shake. The gyro sensor 180B may also be used for navigating, somatosensory game scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, electronic device 100 calculates altitude from barometric pressure values measured by barometric pressure sensor 180C, aiding in positioning and navigation.

The magnetic sensor 180D includes a hall sensor. The electronic device 100 may detect the opening and closing of the flip cover using the magnetic sensor 180D. In some embodiments, when the electronic device 100 is a flip machine, the electronic device 100 may detect the opening and closing of the flip according to the magnetic sensor 180D. And then according to the detected opening and closing state of the leather sheath or the opening and closing state of the flip, the characteristics of automatic unlocking of the flip and the like are set.

The acceleration sensor 180E may detect the magnitude of acceleration of the electronic device 100 in various directions (typically three axes). The magnitude and direction of gravity may be detected when the electronic device 100 is stationary. The electronic equipment gesture recognition method can also be used for recognizing the gesture of the electronic equipment, and is applied to horizontal and vertical screen switching, pedometers and other applications.

A distance sensor 180F for measuring a distance. The electronic device 100 may measure the distance by infrared or laser. In some embodiments, the electronic device 100 may range using the distance sensor 180F to achieve quick focus.

The proximity light sensor 180G may include, for example, a Light Emitting Diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. The electronic device 100 emits infrared light outward through the light emitting diode. The electronic device 100 detects infrared reflected light from nearby objects using a photodiode. When sufficient reflected light is detected, it may be determined that there is an object in the vicinity of the electronic device 100. When insufficient reflected light is detected, the electronic device 100 may determine that there is no object in the vicinity of the electronic device 100. The electronic device 100 can detect that the user holds the electronic device 100 close to the ear by using the proximity light sensor 180G, so as to automatically extinguish the screen for the purpose of saving power. The proximity light sensor 180G may also be used in holster mode, pocket mode to automatically unlock and lock the screen.

The ambient light sensor 180L is used to sense ambient light level. The electronic device 100 may adaptively adjust the brightness of the display 194 based on the perceived ambient light level. The ambient light sensor 180L may also be used to automatically adjust white balance when taking a photograph. Ambient light sensor 180L may also cooperate with proximity light sensor 180G to detect whether electronic device 100 is in a pocket to prevent false touches.

The fingerprint sensor 180H is used to collect a fingerprint. The electronic device 100 may utilize the collected fingerprint feature to unlock the fingerprint, access the application lock, photograph the fingerprint, answer the incoming call, etc.

The temperature sensor 180J is for detecting temperature. In some embodiments, the electronic device 100 performs a temperature processing strategy using the temperature detected by the temperature sensor 180J. For example, when the temperature reported by temperature sensor 180J exceeds a threshold, electronic device 100 performs a reduction in the performance of a processor located in the vicinity of temperature sensor 180J in order to reduce power consumption to implement thermal protection. In other embodiments, when the temperature is below another threshold, the electronic device 100 heats the battery 142 to avoid the low temperature causing the electronic device 100 to be abnormally shut down. In other embodiments, when the temperature is below a further threshold, the electronic device 100 performs boosting of the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperatures.

The touch sensor 180K, also referred to as a "touch panel". The touch sensor 180K may be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is for detecting a touch operation acting thereon or thereabout. The touch sensor may communicate the detected touch operation to the application processor to determine the touch event type. Visual output related to touch operations may be provided through the display 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100 at a different location than the display 194.

The bone conduction sensor 180M may acquire a vibration signal. In some embodiments, bone conduction sensor 180M may acquire a vibration signal of a human vocal tract vibrating bone pieces. The bone conduction sensor 180M may also contact the pulse of the human body to receive the blood pressure pulsation signal. In some embodiments, bone conduction sensor 180M may also be provided in a headset, in combination with an osteoinductive headset. The audio module 170 may analyze the voice signal based on the vibration signal of the sound portion vibration bone block obtained by the bone conduction sensor 180M, so as to implement a voice function. The application processor may analyze the heart rate information based on the blood pressure beat signal acquired by the bone conduction sensor 180M, so as to implement a heart rate detection function.

The keys 190 include a power-on key, a volume key, etc. The keys 190 may be mechanical keys. Or may be a touch key. The electronic device 100 may receive key inputs, generating key signal inputs related to user settings and function controls of the electronic device 100.

The motor 191 may generate a vibration cue. The motor 191 may be used for incoming call vibration alerting as well as for touch vibration feedback. For example, touch operations acting on different applications (e.g., photographing, audio playing, etc.) may correspond to different vibration feedback effects. The motor 191 may also correspond to different vibration feedback effects by touching different areas of the display screen 194. Different application scenarios (such as time reminding, receiving information, alarm clock, game, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect may also support customization.

The indicator 192 may be an indicator light, may be used to indicate a state of charge, a change in charge, a message indicating a missed call, a notification, etc.

The SIM card interface 195 is used to connect a SIM card. The SIM card may be inserted into the SIM card interface 195, or removed from the SIM card interface 195 to enable contact and separation with the electronic device 100. The electronic device 100 may support 1 or N SIM card interfaces, N being a positive integer greater than 1. The SIM card interface 195 may support Nano SIM cards, micro SIM cards, and the like. The same SIM card interface 195 may be used to insert multiple cards simultaneously. The types of the plurality of cards may be the same or different. The SIM card interface 195 may also be compatible with different types of SIM cards. The SIM card interface 195 may also be compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to realize functions such as communication and data communication. In some embodiments, the electronic device 100 employs esims, i.e.: an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.

The software system of the electronic device 100 may employ a layered architecture, an event driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture. In the embodiment of the invention, taking an Android system with a layered architecture as an example, a software structure of the electronic device 100 is illustrated.

Fig. 2B is a software architecture block diagram of the electronic device 100 according to an embodiment of the present invention.

The layered architecture divides the software into several layers, each with distinct roles and branches. The layers communicate with each other through a software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, an application layer, an application framework layer, an Zhuoyun row (Android run) and system libraries, and a kernel layer, respectively.

The application layer may include a series of application packages.

As shown in fig. 2B, the application package may include applications for cameras, gallery, calendar, talk, map, navigation, WLAN, bluetooth, music, video, short message, etc.

The application framework layer provides an application programming interface (application programming interface, API) and programming framework for application programs of the application layer. The application framework layer includes a number of predefined functions.

As shown in FIG. 2B, the application framework layer may include a window manager, a content provider, a view system, a telephony manager, a resource manager, a notification manager, and the like.

The window manager is used for managing window programs. The window manager can acquire the size of the display screen, judge whether a status bar exists, lock the screen, intercept the screen and the like.

The content provider is used to store and retrieve data and make such data accessible to applications. The data may include video, images, audio, calls made and received, browsing history and bookmarks, phonebooks, etc.

The view system includes visual controls, such as controls to display text, controls to display pictures, and the like. The view system may be used to build applications. The display interface may be composed of one or more views. For example, a display interface including a text message notification icon may include a view displaying text and a view displaying a picture.

The telephony manager is used to provide the communication functions of the electronic device 100. Such as the management of call status (including on, hung-up, etc.).

The resource manager provides various resources for the application program, such as localization strings, icons, pictures, layout files, video files, and the like.

The notification manager allows the application to display notification information in a status bar, can be used to communicate notification type messages, can automatically disappear after a short dwell, and does not require user interaction. Such as notification manager is used to inform that the download is complete, message alerts, etc. The notification manager may also be a notification in the form of a chart or scroll bar text that appears on the system top status bar, such as a notification of a background running application, or a notification that appears on the screen in the form of a dialog window. For example, a text message is prompted in a status bar, a prompt tone is emitted, the electronic device vibrates, and an indicator light blinks, etc.

Android run time includes a core library and virtual machines. Android run time is responsible for scheduling and management of the Android system.

The core library consists of two parts: one part is a function which needs to be called by java language, and the other part is a core library of android.

The application layer and the application framework layer run in a virtual machine. The virtual machine executes java files of the application program layer and the application program framework layer as binary files. The virtual machine is used for executing the functions of object life cycle management, stack management, thread management, security and exception management, garbage collection and the like.

The system library may include a plurality of functional modules. For example: surface manager (surface manager), media Libraries (Media Libraries), three-dimensional graphics processing Libraries (e.g., openGL ES), 2D graphics engines (e.g., SGL), etc.

The surface manager is used to manage the display subsystem and provides a fusion of 2D and 3D layers for multiple applications.

Media libraries support a variety of commonly used audio, video format playback and recording, still image files, and the like. The media library may support a variety of audio and video encoding formats, such as MPEG4, h.264, MP3, AAC, AMR, JPG, PNG, etc.

The three-dimensional graphic processing library is used for realizing three-dimensional graphic drawing, image rendering, synthesis, layer processing and the like.

The 2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is a layer between hardware and software. The inner core layer at least comprises a display driver, a camera driver, an audio driver and a sensor driver.

The workflow of the electronic device 100 software and hardware is illustrated below in connection with capturing a photo scene.

When touch sensor 180K receives a touch operation, a corresponding hardware interrupt is issued to the kernel layer. The kernel layer processes the touch operation into the original input event (including information such as touch coordinates, time stamp of touch operation, etc.). The original input event is stored at the kernel layer. The application framework layer acquires an original input event from the kernel layer, and identifies a control corresponding to the input event. Taking the touch operation as a touch click operation, taking a control corresponding to the click operation as an example of a control of a camera application icon, the camera application calls an interface of an application framework layer, starts the camera application, further starts a camera driver by calling a kernel layer, and captures a still image or video by the camera 193.

The following describes a schematic structural diagram of the cloud server 200 according to the embodiment of the present application.

Fig. 2C is a schematic structural diagram of a cloud server 200 according to an embodiment of the present application.

As shown in fig. 2C, the cloud server 200 includes one or more processors 201, a communication interface 202, and a memory 203, where the processors 201, the communication interface 202, and the memory 203 may be connected by a bus or other means, and in this embodiment, the connection is exemplified by a bus 204. Wherein:

the processor 201 may be constituted by one or more general-purpose processors, such as a CPU. The processor 201 may be used to run the relevant program code of the device control method.

The communication interface 202 may be a wired interface (e.g., an ethernet interface) or a wireless interface (e.g., a cellular network interface or using a wireless local area network interface) for communicating with other nodes. In the embodiment of the present application, the communication interface 202 is specifically used to communicate with the electronic device 100.

Memory 203 may include volatile memory (RAM), such as; the memory may also include a non-volatile memory (non-volatile memory), such as ROM, flash memory (flash memory), HDD, or solid state disk SSD; memory 203A may also include a combination of the above types of memory. The memory 203 may be used to store a set of program codes such that the processor 201 invokes the program codes stored in the memory 203 to implement the server-implemented method of the embodiments of the present application.

It should be noted that the server shown in fig. 2C is only one implementation of the embodiment of the present application, and in practical applications, the server may further include more or fewer components, which is not limited herein.

The following describes an image reconstruction method provided in the embodiments of the present application in conjunction with an application scenario.

In some application scenarios, a user may open a camera application to take a picture while taking a picture using the electronic device 100. When the electronic device 100 receives a photographing operation of a user, the electronic device 100 acquires a low-definition image photographed by a camera, identifies and extracts image features (such as ferris wheel buildings) of specified target content in the low-definition image, and matches high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library of the same category as the specified target content on a cloud server. The electronic device 100 may fuse the high definition texture feature into a low definition image to obtain a high definition image. In this way, the imaging quality of the specified target content can be improved.

For example, as shown in fig. 3A, the electronic device 100 may display an interface 310 with a home screen, where the interface 310 displays a page with application icons, including a plurality of application icons (e.g., weather application icons, stock application icons, calculator application icons, setup application icons, mail application icons, payment device application icons, facebook application icons, browser application icons, gallery application icons 312, music application icons, video application icons, weChat application icons, etc.). Page indicators are also displayed below the application icons to indicate the positional relationship between the currently displayed page and other pages. Below the page indicator are a plurality of tray icons (e.g., dialing application icon, information application icon, contact application icon, camera application icon 313) that remain displayed when the page is switched. In some embodiments, the page may also include a plurality of application icons and a page indicator, which may not be part of the page, but may exist alone, and the picture icon may also be optional, which is not limited in this embodiment of the present application. A status bar 311 is displayed in an upper partial area of the interface 310, and the status bar 311 may include: one or more signal strength indicators of mobile communication signals (also may be referred to as cellular signals), battery status indicators, time indicators, wi-Fi signal indicators, and so forth.

The electronic device 100 may receive an input operation (e.g., a click) by a user on the camera application icon 513, and in response to the input operation, the electronic device 100 may display a photographing preview interface 520 as shown in fig. 5B.

As shown in fig. 3B, the shot preview interface 320 can display a preview screen 324 including a shot image playback control 321, a shooting control 322, a camera conversion control 323, a camera capture, a setup control 325, a zoom magnification control 326, one or more shooting mode controls (e.g., a "night scene mode" control 327A, a "portrait mode" control 327B, a "normal shooting mode" control 327C, a "video mode" control 327D, a "professional mode" control 327E, a more mode control 327F, a "large aperture mode" control 327H, etc.). Wherein the captured image return control 321 is operable to display a captured image. The capture control 322 is used to trigger saving of images captured by the camera. The camera conversion control 323 can be used to switch the camera to take a photograph. The settings control 325 may be used to set the photographing function. The zoom magnification control 326 may be used to set the zoom magnification of the camera. The shooting mode control can be used for triggering and starting an image processing flow corresponding to the shooting mode. For example, a "night scene mode" control 327A may be used to trigger an increase in brightness and color richness in the captured image, and so forth. The "portrait mode" control 372B may be used to trigger blurring of the background of a person in a captured image, and so on. As shown in fig. 3B, the photographing mode selected by the current user is the "normal photographing mode".

The electronic device 100 may receive an input operation (e.g., a single click) by a user on a capture control 322 in the capture preview interface 320, in response to which the electronic device 100 may obtain a low-definition image (e.g., a preview screen 324) captured by the camera. The electronic device 100 may identify and extract image features of the specified target content in the low-definition image, and match high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library on the cloud server of the same class as the specified target content. The electronic device 100 may fuse the high definition texture feature into a low definition image to obtain a high definition image.

Alternatively, after the electronic device 100 recognizes the category of the specified target content in the low-definition image, category information 329 may be displayed in the above-mentioned photographing interface 320, and the category information 329 may be used to indicate the category of the specified target content in the low-definition image, for example, the category information 329 may be a word of "ferris wheel".

The description of the super-resolution reconstruction process for the low-definition image may be referred to in the subsequent embodiments of the present application, and will not be repeated here.

In one possible implementation manner, in the process that the electronic device 100 performs the super-resolution reconstruction processing on the captured low-definition image by using the high-definition texture dictionary database on the cloud server 200, the electronic device 100 may display a processing progress prompt or count down, for prompting the user that the current electronic device 100 performs the super-resolution reconstruction processing on the captured low-definition image by using the high-definition texture dictionary database on the cloud server 200.

For example, as shown in fig. 3C, the electronic device 100 may display a progress prompt window 341 on the photographing preview interface 320 for prompting the progress of the super-resolution reconstruction of the photographed low-definition image. For example, the progress prompt window 341 displays a text prompt "photos being taken through cloud optimization, progress is 10%". A cancel control 342 may also be displayed in the progress prompt window 341, which may be used to trigger cancellation of the superscore reconstruction of the captured image. For example, when the electronic device 100 receives an input operation (e.g., a single click) by the user for the cancel control 342, the electronic device 100 may stop performing the super-resolution reconstruction process on the captured image. In this way, the super-resolution reconstruction of the photographed image can be cancelled instantaneously when the progress is slow or when the user does not temporarily want to super-resolution the photographed image.

In one possible implementation, after the electronic device 100 completes enhancement of the captured image by using the processing capability of the cloud server 200, the electronic device 100 may display a completion prompt and store the super-resolution reconstructed high-definition image locally. The completion prompt may be used to prompt the user that the superscore reconstruction has been completed for the captured image.

Illustratively, as shown in fig. 3D, the electronic device 100 displays a completion prompt 343 after completing the super-resolution reconstruction of the captured image by means of the high-definition texture dictionary library on the cloud server 200. For example, completion prompt 343 may be a text prompt "completed photo superprocess, available for review in the gallery".

The electronic device 100 may receive an input operation (e.g., a single click) by the user for the captured image return control 321, in response to which the electronic device 100 may display a photo browsing interface 550 as shown in fig. 3E.

As shown in fig. 3E, the photo browsing interface 350 may include a super-resolution reconstructed high definition image 351, image related information 353, a menu 354, and a gallery control 355. The image related information 353 may include, among other things, a photographing time, photographing weather, geographical location information, etc. of the high definition image 351. For example, the shooting time may be "2019 12, 3, 8:00AM ", shooting weather may be" cloudy ", shooting place may be" Shanghai-Disney ", and the like. The menu 354 may include a share button, a favorites button, an edit button, a delete button, and more buttons. A share button may be used to trigger sharing of the high definition image 351. The collection button may be used to trigger collection of the high definition image 351 to a picture collection folder. The edit button may be used to trigger edit functions such as rotation, clipping, adding filters, blurring, etc. to the high definition image 351. A delete button may be used to trigger deletion of the high definition image 351. More buttons may be used to trigger the turning on of more functions associated with the high definition image 351. The gallery control 355 may be used to trigger the electronic device 100 to open a gallery application.

In some embodiments, a user may open a camera application to take a picture while taking a picture using the electronic device 100. When the electronic device 100 displays the shooting preview interface, the shooting preview interface may include a super-resolution reconstruction control and a shooting control, where the super-resolution reconstruction control may be used to trigger the electronic device 100 to perform super-resolution reconstruction on the captured low-definition image. The electronic device 100 may turn on the superdivision reconstruction function after receiving the user input for the image reconstruction control. After the super-resolution reconstruction function is started, when the electronic device 100 receives a photographing operation of a user, the electronic device 100 acquires a low-definition image photographed by the camera. The electronic device 100 may identify and extract image features of the specified target content in the low-definition image, and match high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library on the cloud server of the same class as the specified target content. The electronic device 100 may fuse the high definition texture feature into a low definition image to obtain a high definition image. In this way, the imaging quality of the specified target content can be improved.

Illustratively, the electronic device 100 may receive an input operation (e.g., a click) by the user on the camera application icon 313 in the interface 310 shown in fig. 3A, and in response to the input operation, the electronic device 100 may display the shooting preview interface 420 shown in fig. 4A.

As shown in fig. 4A, the shot preview interface 420 may display a display screen including a shot image display back control 421, a shot control 422, a camera conversion control 423, a camera captured preview screen 424, a super resolution reconstruction control 428, a setup control, a zoom magnification control, one or more shot mode controls (e.g., a "night view mode" control 427A, a "portrait mode" control 427B, a "plain shot mode" control 427C, a "video mode" control 427D, a "professional mode" control 427E, a more mode control 427F, a "large aperture mode" control 427H, etc.). Wherein the super-resolution reconstruction control 428 is operable to trigger the electronic device 100 to super-resolution reconstruct the captured low-definition image. For the text description of the shot image feedback control 421, the shooting control 422, the camera conversion control 423, the setting control, the zoom magnification control, and the one or more shooting mode controls, reference may be made to the foregoing embodiment shown in fig. 3B, which is not described herein again.

The electronic device 100 can receive an input operation (e.g., a single click) by a user for the superbranch reconstruction control 428, in response to which the electronic device 100 can turn on the image reconstruction function. After turning on the image reconstruction function, the electronic device 100 may receive an input operation (e.g., a single click) by the user for the capture control 422 in the capture preview interface 420, and in response to the input, the electronic device 100 may obtain a low-definition image (e.g., a preview screen 424) captured by the camera. The electronic device 100 may identify and extract image features of the specified target content in the low-definition image, and match high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library on the cloud server of the same class as the specified target content. The electronic device 100 may fuse the high definition texture feature into a low definition image to obtain a high definition image. The electronic device 100 may save the high definition image locally.

In embodiments of the present application, the superminute reconstruction control 428 described above may be referred to as a first control. The input of the superminute reconstruction control 428 may be referred to as a second input.

Alternatively, after the electronic device 100 recognizes the category of the specified target content in the low-definition image, category information 429 may be displayed in the above-mentioned photographing interface 420, and the category information 429 may be used to indicate the category of the specified target content in the low-definition image, for example, the category information 429 may be a word of "ferris wheel".

In some embodiments, a user may take a picture in different modes of taking a picture in the electronic device 100 while taking a picture using the electronic device 100. When the user feels that the resolution of capturing the preview image displayed in the preview interface is low, the user can select the "super-resolution reconstruction mode" in the camera application to capture. In the "super resolution reconstruction mode", when the electronic device 100 receives a photographing operation of a user, the electronic device 100 acquires a low-definition image photographed by the camera. The electronic device 100 may identify and extract image features of the specified target content in the low-definition image, and match high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library on the cloud server of the same class as the specified target content. The electronic device 100 may fuse the high definition texture feature into a low definition image to obtain a high definition image. In this way, the imaging quality of the specified target content can be improved.

For example, the electronic device 100 may receive an input operation (e.g., a single click) by a user on the camera application icon 313 in the interface 310 shown in fig. 3A, and in response to the input operation, the electronic device 100 may display a photographing preview interface 520 shown in fig. 5A.

As shown in fig. 5A, the shot preview interface 520 may display a preview screen 524 including a shot image return control 521, a shot control 522, a camera conversion control 523, a camera captured preview screen, a setup control, a zoom magnification control, one or more shooting mode controls (e.g., a "night scene mode" control 527A, a "portrait mode" control 527B, a "normal shooting mode" control 527C, a "super-resolution reconstruction mode" control 527G, a "video mode" control 527D, a "professional mode" control 527E, a more mode control 527F, etc.). The "super-resolution reconstruction mode" control 527G may be used to trigger the electronic device 100 to perform super-resolution reconstruction on the captured low-definition image. For the text description of the shot image back display control 521, the shooting control 522, the camera conversion control 523, the setting control, the zoom magnification control, and the one or more shooting mode controls, reference may be made to the foregoing embodiment shown in fig. 3B, which is not repeated herein. As shown in fig. 5A, the photographing mode selected by the current user is the "normal photographing mode".

The electronic device 100 may receive an input operation (e.g., a single click) by a user on the "superreconstruction mode" control 527G, in response to which the electronic device 100 may adjust the currently selected shooting mode to the "superreconstruction mode" as shown in fig. 5B.

In embodiments of the present application, the "superreconstruction mode" control 527G described above may be referred to as a first control. The input for the "superreconstruction mode" control 527G may be referred to as a second input.

After the shooting mode of the electronic device 100 is adjusted to the "super-resolution reconstruction mode", the electronic device 100 may receive an input operation (e.g., a click) by a user on the shooting control 522 in the shooting preview interface 520, and in response to the input, the electronic device 100 may acquire a low-definition image (e.g., a preview screen 524) acquired by the camera. The electronic device 100 may identify and extract image features of the specified target content in the low-definition image, and match high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library on the cloud server of the same class as the specified target content. The electronic device 100 may fuse the high definition texture feature into a low definition image to obtain a high definition image. The electronic device 100 may save the high definition image locally.

Alternatively, after the electronic device 100 recognizes the category of the specified target content in the low-definition image, category information 529 may be displayed in the above-mentioned photographing interface 520, and the category information 529 may be used to indicate the category of the specified target content in the low-definition image, for example, the category information 529 may be a word of "ferris wheel".

In some application scenarios, the electronic device 100 may store a number of pictures locally, which may be captured by the electronic device 100, or transmitted by other devices, or downloaded from a network. Wherein, the resolution of partial pictures is not high due to the limitation of the capability of the shooting equipment. The electronic device 100 may obtain a low-definition image in a gallery in response to a user input. The electronic device 100 may identify and extract image features of the specified target content in the low-definition image, and match high-definition texture features similar to the image features of the specified target content based on a high-definition texture dictionary library on the cloud server of the same class as the specified target content. The electronic device 100 may fuse the high definition texture features into a low definition image, resulting in a high definition image, and store it in a gallery. In this way, the imaging quality of the specified target content can be improved.

For example, as shown in fig. 6A, electronic device 100 may receive an input operation (e.g., a single click) by a user on gallery application icon 312 in interface 310, in response to which electronic device 100 may display gallery application interface 610 as shown in fig. 6B.

As shown in fig. 6B, the gallery application interface 610 may display a thumbnail including one or more pictures. The electronic device 100 may mark the one or more pictures that include the specified target content and are not subjected to the super-resolution reconstruction processing. For example, the electronic device 100 may display a mark 612 on the thumbnail 611, the mark 612 being usable to prompt the user for waiting for the super-resolution reconstruction process for the picture corresponding to the thumbnail 611. Alternatively, the electronic device 100 may mark an original picture that has undergone the super-resolution reconstruction process and a high-definition picture after the super-resolution reconstruction process in the one or more pictures.

The electronic device 100 may receive an input operation (e.g., a click) by a user on the thumbnail 611, and in response to the input operation, the electronic device 100 may display a picture browsing interface 620 as shown in fig. 6C.

As shown in fig. 6C, the picture browsing interface 620 may display a picture 621 including the thumbnail 611, a super-resolution reconstruction control 624, picture related information 622, a menu 623, and the like. The picture related information 622 may include one or more of a photographing time, photographing weather, geographical location information, etc. of the picture 621. For example, the shooting time may be "2019 12, 1, 8:00AM ", shooting weather may be" cloudy ", shooting place may be" Shanghai-beach ", and so on. The menu 623 may include a share button, a favorites button, an edit button, a delete button, and a more button. The super-resolution reconstruction control 624 may be used to trigger the electronic device 100 to perform super-resolution reconstruction processing on the picture 621. The process of performing the super-resolution reconstruction processing on the picture 621 may refer to the following embodiments, which are not described herein.

The electronic device 100 may receive an input operation (e.g., a single click) by a user on the superresolution reconstruction control 624, in response to which the electronic device 100 may identify and extract image features of the specified target content in the tile 621 and match out high definition texture features similar to the image features of the specified target content based on a high definition texture dictionary library of the same class as the specified target content on the cloud server. The electronic device 100 may fuse the high definition texture feature into the picture 621 resulting in a high definition picture 631, the high definition picture 631 having a higher resolution than the picture 621.

In embodiments of the present application, the superreconstruction control 624 described above may be referred to as a second control.

As shown in fig. 6D, the electronic device 100 may display the picture browsing interface 630 after obtaining the high-definition picture 631. The image browsing interface 630 may include a high-definition image 631, image related information 632, a menu 633, an effect comparison control 635, a mark 634, a save control 636, and the like. The picture related information 632 may include one or more of photographing time, photographing weather, geographical location information, etc. of the high definition picture 631. For example, the shooting time may be "2019 12, 1, 8:00AM ", shooting weather may be" cloudy ", shooting place may be" Shanghai-beach ", and so on. The menu 633 may include one or more of a share button, a collection button, an edit button, a delete button, a more button, and the like. The effect contrast control 634 may be used to trigger the electronic device 100 to display the high definition picture 631 in place of the picture 621. The marker 634 may be used to prompt the user that the high definition picture 631 is a high definition image after the super resolution reconstruction process. The save control 636 may be used to trigger the electronic device 100 to save the high definition picture 634 into a gallery.

The electronic device 100 may receive an input operation (e.g., a click) of the effect contrast control 635 by the user, and in response to the input operation, as shown in fig. 6E, the electronic device 100 may display the picture browsing interface 620, where the picture 621 is included in the picture browsing interface 620. The electronic device 100 may again receive an input operation (e.g., a single click) by the user for the effect contrast control 635, in response to which the electronic device 100 may display the picture browsing interface 630 shown in fig. 6D described above.

An image reconstruction method provided in the embodiments of the present application is described below.

Fig. 7 is a schematic flow chart of an image reconstruction method according to an embodiment of the present application.

As shown in fig. 7, the method may include the steps of:

s701, the cloud server 200 acquires a plurality of high-quality images and a shooting target category for each high-quality image.

Among other things, the photographic target category may include faces, buildings, animal cats, animal dogs, animal birds, and the like.

Possibly, to subdivide the type of photographic subject, the photographic subject category may include a plurality of subcategories. For example, for a face capture object, subcategories of skin tone, age bracket, gender, race, etc. may be included. For well-known architectural photography targets, sub-categories of viewing angles, seasons, lighting environments, etc. may be included.

For different shooting targets, the cloud server 200 may employ different feature extraction networks to extract high-definition texture features in high-quality images. For example, when extracting face features from high-quality images, it is necessary to consider factors such as age group, skin color, shape of five sense organs, and sex. The cloud server 200 may use a face recognition model as a feature extractor; if plant characteristics are extracted, the characteristics of colors, shapes, textures and the like of plants need to be considered, and a plant classification network can be used as a characteristic extractor.

S702, the cloud server 200 extracts texture features of shooting targets in high-quality images, and establishes a plurality of high-definition texture dictionary libraries of different shooting target categories.

For each shooting target category, the cloud server 200 can extract high-definition texture features of the shooting target category from a plurality of high-quality images under the shooting target category through a feature extractor corresponding to the shooting target category. The cloud server 200 may perform clustering processing on the high-definition texture features under the shooting target category, to obtain the most representative high-definition texture features. The cloud server 200 may store the most representative high-definition texture features under the same shooting target category in the corresponding high-definition texture dictionary library under the shooting target category.

For example, for capturing a plurality of face high-quality images of which the target type is a face, the cloud server 200 may perform the following operations for each face high-quality image: the cloud server 200 may extract features of the face high quality image on different scales. Then, the cloud server 200 may detect face key (landmark) points in the face high-quality image, and crop and resample the five sense organs of the left eye, the right eye, the nose, the mouth, etc. on each scale size so that the features of the five sense organs reach a fixed size. Then, the cloud server 200 may generate K clusters for each five sense organs through a K-means algorithm, to obtain high definition texture features of each five sense organ. The cloud server 200 may extract and store the high-definition texture feature of each five sense organs into the high-definition texture dictionary library corresponding to the face class.

The high-definition texture dictionary library of the plurality of different shooting target categories established on the cloud server 200 may be as shown in the following table 1:

TABLE 1

Shooting target class	High definition texture dictionary library
		Human face	High definition texture dictionary library 1
Building construction	High definition texture dictionary library 2
		Green plant	High definition texture dictionary library 3
Animal hawk	High definition texture dictionary library 4
		…	…

As can be seen from table 1 above, the cloud server 200 may be established with a high definition texture dictionary library 1, a high definition texture dictionary library 2, a high definition texture dictionary library 3, a high definition texture dictionary library 4, and the like. The class of shooting targets of the high-definition texture dictionary library 1 is a face, the class of shooting targets of the high-definition texture dictionary library 2 is a building, the class of shooting targets of the high-definition texture dictionary library 3 is a green plant, and the class of shooting targets of the high-definition texture dictionary library 4 is an animal hawk. The above table 1 is only for exemplary explanation of the present application and should not be construed as limiting.

In some embodiments, to subdivide the type of photographic subject, the photographic subject category may include multiple subcategories. For example, for a face capture object, one or more sub-categories of skin tone, age group, gender, race, etc. may be included. For well-known architectural photography targets, sub-categories of viewing angles, seasons, lighting environments, etc. may be included. The high-definition texture dictionary library of the plurality of different shooting target categories established on the cloud server 200 may be as shown in the following table 2:

TABLE 2

As can be seen from table 1 above, the cloud server 200 may be configured with a high-definition texture dictionary library 1, a high-definition texture dictionary library 2, a high-definition texture dictionary library 3, a high-definition texture dictionary library 4, a high-definition texture dictionary library 5, a high-definition texture dictionary library 6, a high-definition texture dictionary library 7, a high-definition texture dictionary library 8, a high-definition texture dictionary library 9, a high-definition texture dictionary library 10, a high-definition texture dictionary library 11, a high-definition texture dictionary library 12, a high-definition texture dictionary library 13, a high-definition texture dictionary library 14, a high-definition texture dictionary library 15, a high-definition texture dictionary library 16, a high-definition texture dictionary library 17, a high-definition texture dictionary library 18, and the like. Wherein, the shooting target category of the high-definition texture dictionary library 1 is the young male face of yellow skin. The photographing target class of the high-definition texture dictionary library 2 is the young female face of yellow skin. The shooting target class of the high-definition texture dictionary library 3 is the middle-aged male face of yellow skin. The shooting target class of the high-definition texture dictionary library 4 is the face of a middle-aged female with yellow skin. The photographing target class of the high-definition texture dictionary library 5 is the aged male face of yellow skin. The photographing target class of the high-definition texture dictionary library 6 is the face of an aged female with yellow skin. The photographing target class of the high definition texture dictionary library 7 is the young man face of black skin. The photographing target class of the high-definition texture dictionary library 8 is the face of young females with black skin. The shooting target class of the high-definition texture dictionary library 9 is the middle-aged male face of black skin. The photographing target class of the high definition texture dictionary library 10 is the middle-aged female face of black skin. The photographing target class of the high definition texture dictionary library 11 is the aged male face of black skin. The photographing target class of the high definition texture dictionary library 12 is the elderly female face of black skin. The photographing target class of the high definition texture dictionary library 13 is the young man face of white skin. The photographing target class of the high definition texture dictionary library 14 is the young female face of white skin. The photographing target class of the high definition texture dictionary library 15 is a middle-aged male face of white skin. The class of photographic targets of the high definition texture dictionary library 16 is the middle-aged female face of white skin. The photographing target class of the high definition texture dictionary library 17 is the aged male face of white skin. The class of photographic targets of the high definition texture dictionary library 18 is the elderly female face with white skin. The above table 2 is only for exemplary explanation of the present application and should not be construed as limiting.

S703, the electronic device 100 receives a first input.

S704, the electronic device 100 acquires a low-definition image in response to the first input.

For example, the first input may be an input to the shooting control 322 in the shooting preview interface 320 shown in fig. 3B, an input to the shooting control 422 in the shooting preview interface 420 shown in fig. 4B, or an input to the shooting control 522 in the shooting preview interface 520 shown in fig. 5B. In response to the first input, the electronic device 100 may acquire a low-definition image captured by the camera.

In one possible implementation, the first input may also be an input to the super-resolution reconstruction control 624 in the picture browse interface 620 shown in fig. 6C, described above. In response to the first input, the electronic device 100 may determine the picture 621 displayed in the picture browsing interface 620 shown in fig. 6C described above as a low-definition image.

S705, the electronic device 100 determines whether the specified target content is included in the low-definition image.

Specifically, the electronic device 100 is preset with recognition algorithm models corresponding to different shooting target categories. The electronic device 100 may detect whether the low-definition image includes the specified target content through the recognition algorithm models corresponding to the different shooting target categories, respectively. The category of the specified target content may include any one of image content such as a face, a building, a green plant, an animal cat, an animal dog, an animal bird, and the like.

In one possible implementation, to subdivide the type of shooting target, the type of specified target content may include multiple sub-types. For example, for a face capture object, sub-types of skin tone, age group, gender, race, etc. may be included. For well-known architectural shooting targets, subtypes of viewing angles, seasons, lighting environments, etc. may be included.

In one possible implementation, the plurality of recognition algorithm models preset on the electronic device 100 may recognize that the capturing target class set is the same as the class set of the high-definition texture dictionary library stored on the cloud server 200. For example, the set of photographic target categories that may be identified by the plurality of recognition algorithm models on the electronic device 100 may include faces, buildings, and the like. The set of shooting target categories of the high definition texture dictionary library stored on the cloud server 200 may also include faces, buildings, and the like.

In one possible implementation, when cloud server 200 detects that the set of categories of the high definition texture dictionary library differs from the set of identification categories of the target identification model in electronic device 100, cloud server 200 may send target identification model update information to electronic device 100. The electronic device 100 may update the object recognition algorithm model based on the update information. The updated target recognition algorithm model can recognize that the class set of shooting targets is the same as the class set of the high-definition texture dictionary library stored on the cloud server 200.

In one possible implementation, multiple recognition algorithm models on the electronic device 100 may recognize the class of the photographic subject. While the plurality of high definition texture dictionary libraries on the cloud server 200 also have a plurality of subcategories under each class of photographic targets. For example, the set of photographic target categories that may be identified by the plurality of recognition algorithm models of the electronic device 100 may include faces, buildings, and the like. The categories of the plurality of high definition texture dictionary libraries stored on cloud server 200 may include faces, buildings, and the like. The face categories may also include yellow skin faces, black skin faces, white skin faces, and the like. After the electronic device 100 sends the face types and the image features to the cloud server 200, the cloud server 200 may first screen out the high-definition texture dictionary library corresponding to the yellow skin type face, the high-definition texture dictionary library corresponding to the black skin type face, and the high-definition texture dictionary library corresponding to the white skin type face. Then, the cloud server 200 may identify the race color in the image feature uploaded by the electronic device 100, and determine a high-definition texture dictionary library of the same race color as the image feature from a plurality of high-definition texture dictionary libraries of the face class based on the race color.

In the embodiment of the present application, the specified target content in the low-definition image may be referred to as first specified target content.

If the low-definition image includes the specified target content, the electronic device 100 may execute S706, where the electronic device 100 determines that the category of the specified target content in the low-definition image is the first category and the first area where the specified target content is located.

S707, the electronic device 100 may crop a first area where the specified target content is located from the low-definition image, to obtain a first low-definition cropped image.

The low definition image may be as shown in fig. 8A, for example. The electronic device 100 generates a binary mask (mask) image of a low-definition image after identifying a first region in which a specified target content (e.g., an hawk) of the low-definition image is located.

As shown in fig. 8B, the first area of the binary mask image where the specified target content is located may be displayed as white, and the other positions may be displayed as black. The electronic device 100 may determine coordinates of a crop frame in the low-definition image based on the binary mask image, where the position of the crop frame includes the first area. The position of the crop box in the low definition image may be as shown in fig. 8C.

Based on the cropping frame, the electronic device 100 may crop a first region where the specified target content is located from the low-definition image, to obtain a first low-definition cropped image. Wherein the first low definition cropped image may be as shown in fig. 8D.

S708, the electronic device 100 may extract the first image feature of the low definition clip image.

The first image feature may include a feature vector or a feature map of information such as contour information, color information, texture information, and the like of the specified target content in the low definition cut image.

S709, the electronic device 100 may send the first image feature of the first low definition clip image and the first category to the cloud server 200.

S710, the cloud server 200 may determine that the shooting target class is the first high-definition texture dictionary library of the first class.

S711, the cloud server 200 may match out a first high-definition texture feature similar to the first image feature from the first high-definition texture dictionary library.

The first high-definition texture feature may include a feature vector or a feature map specifying information such as high-definition contour information, high-definition color information, high-definition texture information, and the like corresponding to the target content.

Specifically, the cloud server 200 may match a first high-definition texture feature having a content similarity with the first image feature greater than a preset value from the first high-definition texture dictionary library.

S712, the cloud server 200 may send the first high definition texture feature to the electronic device 100.

S713, the electronic device 100 may fuse the first high-definition texture feature into the first low-definition cropped image to obtain the first high-definition cropped image.

S714, the electronic device 100 replaces the first low-definition cropping image with the first high-definition cropping image, and pastes the first high-definition cropping image back to the first region in the low-definition image, so as to obtain the high-definition image.

The first high definition cropped image may be, for example, as shown in fig. 8E. Wherein the resolution of the first high definition cropped image is greater than the resolution in the first low definition cropped image. The high definition image may be as shown in fig. 8F.

For another example, when the specified target class included in the low-definition image is a face, the first may refer to an area in which the face is located in the low-definition image. The electronic device 100 may extract face keypoints from low-definition images. The electronic device 100 may acquire the five sense organ information, the hair information, and the like in the low-definition image based on the face key points. The electronic device 100 may generate a binary mask image of the low-definition image based on the facial features information, where the binary mask image of the low-definition image may set a display color of a location where the facial features are located to white and display colors of other locations to black. Therefore, the electronic device 100 may determine coordinates of the face frame in the low-definition image based on the binary mask image. The electronic device 100 may expand the area of the face box on the low-definition image. The electronic device 100 may crop out the image in the face box based on the enlarged face box to obtain a first low definition cropped image. The electronic apparatus 100 extracts feature information of each five-element position from the low-definition image based on the five-element information. Then, the electronic device 100 may transmit the feature information of each five-element position and the face class to the cloud server 200. The cloud server 200 may match out a high definition texture dictionary library corresponding to the face class. The cloud server 200 may match the high-definition texture feature corresponding to the feature information of each five sense organs based on the high-definition texture dictionary library corresponding to the face class. The cloud server 200 may transmit the high definition texture feature of each five sense organ to the electronic device 100. The electronic device 100 may fuse the high-definition texture feature corresponding to each five sense organs into the first low-definition clipping image to obtain the first high-definition clipping image. Then, the electronic device 100 may paste the first high-definition cut image back to the face area in the low-definition image based on the binary mask image of the five sense organs information, to obtain the high-definition image.

S715, the electronic device 100 may save the high-definition image.

In one possible implementation, the electronic device 100 may store the low-definition image as well as the high-definition image.

In one possible implementation, the electronic device 100 may also display the high definition image while preserving the high definition image.

In one possible implementation, if the low-definition image does not include the specified target content, the electronic device 100 may perform super-resolution reconstruction on the low-definition image using a general super-resolution processing policy, to obtain a high-definition image. The general super processing strategy comprises the steps of inputting a low-definition image into a super network model for super reconstruction, and obtaining a high-definition image. The super-resolution network model includes super-resolution reconstruction generation type countermeasure networks (SRGAN), enhanced super-resolution reconstruction generation type countermeasure networks (ESRGAN), widely activated efficient and accurate image super-resolution reconstruction (WDSR) networks, and the like.

In some embodiments, when the electronic device 100 may identify a plurality of specified target contents from the acquired low-definition image, the electronic device 100 may extract image features of the plurality of specified target contents and categories of the respective specified target contents, respectively. The plurality of specified target contents may be identical in category, different in category, or different in category. The electronic device 100 may transmit the image characteristics of each specified target content and the category of each specified target content to the cloud server 200. The cloud server 200 may match the high-definition texture dictionary library corresponding to each of the classes of the specified target content, and match the high-definition texture features of each of the specified target content based on the high-definition texture dictionary library corresponding to each of the classes of the specified target content. The cloud server 200 may send the high-definition texture feature of each of the specified target contents to the electronic device 100, and the electronic device 100 may fuse the high-definition texture features of each of the specified target contents into the respective areas of the specified target contents in the low-definition image, so as to obtain the high-definition image. Thus, the electronic device 100 can synchronously perform super-resolution reconstruction processing on a plurality of specified targets in the low-definition image, thereby improving the super-resolution processing efficiency.

In some embodiments, after the low-definition image is acquired, the electronic device 100 may identify and crop the area where the specified target content is located from the low-definition image, so as to obtain a low-definition cropped image and a category of the specified target content. The electronic device 100 may send the low definition clip image and the category of the specified target content to the cloud server 200. The cloud server 200 may extract the first image feature from the low definition cropped image. And matching the first high-definition texture features of which the shooting target type is the same as the category of the appointed target content and the content similarity with the first image features is larger than a preset value. The cloud server 200 may fuse the first high definition texture feature into a first low definition cropped image to obtain the first high definition cropped image. The cloud server 200 may send the first high definition clip image to the electronic device 100. The electronic device 100 may paste the first high-definition cropped image back to the first area where the specified target content is located in the low-definition image, to obtain the high-definition image.

In some embodiments, the cloud server 200 may store a plurality of high-definition image libraries of different shooting target types, where each high-definition image library of a shooting target type includes a plurality of high-definition reference images of the shooting target type. After the electronic device 100 acquires the low-definition image, the area where the specified target content is located may be identified and cut out from the low-definition image, so as to obtain a low-definition cut-out image and a category of the specified target content. The electronic device 100 may send the low definition clip image and the category of the specified target content to the cloud server 200. The cloud server 200 may match high definition guide images, which have the same shooting target type as the category of the specified target content and have a content similarity with the low definition cut image greater than a preset value, based on the categories of the low definition cut image and the specified target content. The cloud server 200 may transmit the high-definition boot image to the electronic device 100. The electronic device 100 may fuse the high-definition texture features in the high-definition guide image into the low-definition cropping image to obtain the high-definition cropping image. The electronic device 100 may paste the high-definition clip image back to the region of the low-definition image where the specified target content is located, to obtain a high-definition image.

Optionally, the cloud server 200 may fuse the high-definition texture features in the high-definition guiding image to the low-definition clipping image based on the high-definition guiding image to obtain the high-definition clipping image. The cloud server 200 may transmit the high definition clip image to the electronic device 100. The electronic device 100 may paste the high-definition clip image back to the region of the low-definition image where the specified target content is located, to obtain a high-definition image.

According to the image reconstruction method provided by the embodiment of the application, the electronic device 100 extracts the image features of the specified target content from the acquired low-definition image, and matches the high-definition texture features similar to the image features of the specified target content based on the high-definition texture dictionary library of the same class as the specified target content on the cloud server. The electronic device may fuse the high definition texture feature into a low definition image to obtain a high definition image. In this way, the super-division reconstruction can be targeted to the specific target, and the operand on the electronic device 100 in the super-division reconstruction process is simplified.

An image reconstruction method according to another embodiment of the present application is described below.

Fig. 9 shows a flowchart of an image reconstruction method according to an embodiment of the present application. As shown in fig. 9, the method may include the steps of:

S901, the cloud server 200 acquires a plurality of high-quality images and a shooting target category for each high-quality image.

For details, reference may be made to step S701 in the embodiment shown in fig. 7 described above.

S902, the cloud server 200 can extract texture features of shooting targets in high-quality images, and establish high-definition texture dictionary libraries of a plurality of different shooting target categories.

For details, reference may be made to step S702 in the embodiment shown in fig. 7.

S903, the electronic device 100 displays a shooting preview interface, on which a shooting key and a first preview image are displayed.

The shooting preview interface may be, for example, the shooting preview interface 320 shown in fig. 3B, the shooting preview interface 420 shown in fig. 4B, or the shooting preview interface 520 shown in fig. 5B.

S904, the electronic device 100 determines whether the first preview image includes the specified target content.

The type of the specified target content may include any one of image content such as a human face, a building, a green plant, an animal cat, an animal dog, an animal bird, and the like.

In the embodiment of the present application, the specified target content in the first preview image may be referred to as second specified target content.

If the first preview image includes the specified target content, the electronic device 100 may determine that the category of the specified target content in the first preview image is the second category and the second area where the specified target content is located in S905.

S906, the electronic device 100 cuts out a second area where the specified target content is located from the first preview image, and a second low-definition cut-out image is obtained.

The electronic device 100 may generate the binary mask image of the first preview image after identifying the second area where the specified target content of the first preview image is located. The binary mask image of the first preview image may display the second area where the specified target content is located in the low-definition image as white and other positions as black. The electronic device 100 may determine coordinates of the crop box in the first preview image based on the binary mask image of the first preview image. The position of the crop box in the first preview image includes the second region. Based on the cropping frame, the electronic device 100 may crop a second region where the specified target content is located from the first preview image, resulting in a second low-definition cropped image.

S907, the electronic device 100 extracts a second image feature of the second low definition clip image.

S908, the electronic device 100 transmits the second image feature and the second class to the cloud server 200.

S909, the cloud server 200 determines that the shooting target class is the second high-definition texture dictionary library of the second class.

S910, the cloud server 200 matches a second high-definition texture feature similar to the second image feature from the second high-definition texture dictionary library.

Specifically, the cloud server 200 may match the second high-definition texture feature with the content similarity with the second image feature being greater than the preset value from the second high-definition texture dictionary library.

S911, the cloud server 200 transmits the second high-definition texture feature to the electronic device 100.

S912, the electronic device 100 stores the second high-definition texture feature in the buffer queue.

The cache queue may be a storage area in the running memory. After the electronic device 100 obtains the second high-definition texture feature, the second high-definition texture feature may be saved in the cache queue.

In some embodiments, the electronic device 100 may further obtain a second preview image captured by the camera, and the electronic device 100 may determine whether the second preview image includes the specified target content, if so, the electronic device 100 may determine that the category of the specified target content in the second preview image is a third category and a third area where the specified target content is located. The electronic device 100 may crop a third region where the specified target content is located from the second preview image, resulting in a third low definition cropped image. The electronic device 100 may extract a third image feature of a third low definition clip image. The electronic device 100 may determine whether the similarity between the third image feature and the second high-definition texture feature stored in the buffer queue is greater than a preset value, and if so, the electronic device 100 may fuse the second high-definition texture feature stored in the buffer queue into the third low-definition clipping image to obtain the third high-definition clipping image.

In the embodiment of the present application, the specified target content in the third preview image may be referred to as third specified target content.

When the similarity between the third image feature and the second high-definition texture feature stored in the cache queue is smaller than the preset value, the electronic device 100 may send the third image feature and the third category to the cloud server 200. The cloud server 200 determines a third high-definition texture dictionary library corresponding to the third category, and matches third high-definition texture features similar to the third image features from the third high-definition texture dictionary library. The cloud server 200 may send the third high definition texture feature to the electronic device 100. After the electronic device 100 obtains the third high-definition texture feature, the third high-definition texture feature may be fused into the third low-definition cropping image to obtain the third high-definition cropping image.

After obtaining the third high-definition cropping image, the electronic device 100 may paste the third high-definition cropping image back to the third area in the second preview image to obtain the high-definition preview image. The electronic device 100 may display the high definition preview image on the capture preview interface.

In this way, the resolution of the preview screen of the electronic device 100 during the preview shooting process can be improved.

S913, the electronic device 100 receives the first input, and acquires a low-definition image.

S914, the electronic device 100 determines whether the low-definition image includes the specified target content.

If so, execution S915 is performed, and the electronic device 100 determines that the category of the specified target content in the low-definition image is the first category and the first area where the specified target content is located.

S916, the electronic device 100 cuts out a first area where the specified target content is located from the low-definition image, and obtains a first low-definition cut-out image.

For details, reference may be made to step S707 in the embodiment shown in fig. 7, which is not described herein.

S917, the electronic device 100 may extract the first image feature of the first low definition clip image.

For details, reference may be made to step S708 in the embodiment shown in fig. 7, which is not described herein.

S918, the electronic device 100 may determine whether the similarity between the first image feature and the high-definition texture feature in the cache queue is greater than a preset value.

If the similarity between the first image feature and the high-definition texture feature in the buffer queue is greater than the preset value, the executing S919, the electronic device 100 may fuse the high-definition texture feature in the buffer queue into the first low-definition clipping image, to obtain the first high-definition clipping image.

For example, the second high-definition texture features are stored in the buffer queue, the electronic device 100 may determine whether the similarity between the first image features and the second high-definition texture features is greater than a preset value, and if so, the electronic device 100 may fuse the second high-definition texture features in the buffer queue into the first clipping image to obtain the first high-definition clipping image.

For another example, the second high-definition texture feature and the third high-definition texture feature are stored in the buffer queue, and the electronic device 100 may calculate the similarity of the first image feature to the second high-definition texture feature and the second high-definition texture feature, respectively. If the similarity between the first image feature and the second high-definition texture feature is greater than a preset value and the similarity between the first image feature and the second high-definition texture feature is less than or equal to a preset value, the electronic device 100 may fuse the second high-definition texture feature into the first clipping image to obtain the first high-definition clipping image.

If the similarity between the first image feature and the second high-definition texture feature is smaller than or equal to a preset value and the similarity between the first image feature and the second high-definition texture feature is larger than the preset value, the electronic device 100 may fuse the third high-definition texture feature into the first clipping image to obtain the first high-definition clipping image.

If the similarity between the first image feature and the second high-definition texture feature is greater than the preset value and the similarity between the first image feature and the second high-definition texture feature is greater than the preset value, the electronic device 100 may compare the similarity between the first image feature and the second high-definition texture feature with the similarity between the first image feature and the third high-definition texture feature. When the similarity between the first image feature and the second high-definition texture feature is greater than the similarity between the first image feature and the third high-definition texture feature, the electronic device 100 may fuse the second high-definition texture feature into the first clipping image to obtain the first high-definition clipping image. When the similarity between the first image feature and the second high-definition texture feature is less than or equal to the similarity between the first image feature and the third high-definition texture feature, the electronic device 100 may fuse the third high-definition texture feature into the first clipping image to obtain the first high-definition clipping image.

If the similarity between the first image feature and the second high-definition texture feature is greater than the preset value, steps S920 to S924 are performed. Wherein:

s920, the electronic device 100 may send the first image feature and the first category to the cloud server 200.

The first category may be the same as or different from the second category and the third category.

S921, the electronic device 100 may determine a first high-definition texture dictionary library with the shooting target class being the first class.

For details, reference may be made to step S710 shown in fig. 7, which is not described herein.

S922, the cloud server 200 may match a first high-definition texture feature similar to the first image feature from the first high-definition texture dictionary library.

S923, the cloud server 200 may send the first high definition texture feature to the electronic device 100.

S924, the electronic device 100 may fuse the first high-definition texture feature into the first low-definition cropped image to obtain the first high-definition cropped image.

S925, the electronic device 100 may replace the first low-definition cropping image with the first high-definition cropping image, and paste the first high-definition cropping image back to the first area in the low-definition image, to obtain the high-definition image.

For details, reference may be made to step S714 in the embodiment shown in fig. 7, which is not described herein.

S926, the electronic device 100 may save the high-definition image.

The specific details may refer to step S715 in the embodiment shown in fig. 7, which is not described herein.

According to the image reconstruction method provided by the embodiment of the application, the electronic device 100 can utilize the image features of the specified target content in the preview image captured by the camera before photographing to match the high-definition texture features similar to the image features of the specified target content from the high-definition texture dictionary library of the same class as the specified target content on the cloud server. When photographing, the electronic equipment can fuse the high-definition texture features into the photographed low-definition image to obtain the high-definition image. In this way, the target content in the preview image can be used to obtain the high-definition texture features in advance and then fused into the photographed low-definition image, so that the resolution of the photograph taken by the electronic device 100 is improved, and the time in the super-resolution reconstruction process is shortened.

Fig. 10 shows a flowchart of an image reconstruction method according to an embodiment of the present application. As shown in fig. 10, the method may include the steps of:

S1001, the cloud server 200 acquires a plurality of high-quality images and a shooting target category for each high-quality image.

S1002, the cloud server 200 can extract texture features of shooting targets in high-quality images, and establish high-definition texture dictionary libraries of a plurality of different shooting target categories.

S1003, the cloud server 200 may send a dictionary library resource package to the electronic device 100, where the dictionary library resource package includes a plurality of high-definition texture dictionary libraries.

S1004, the electronic device receives a first input.

S1005, the electronic apparatus 100 acquires a low-definition image in response to the first input.

S1006, the electronic device 100 determines whether the low-definition image includes the specified target content.

If the low-definition image includes the specified target content, S1007 is executed, and the electronic device 100 determines that the category of the specified target content in the low-definition image is the first category.

S1008, the electronic device 100 may determine whether the dictionary library resource package includes a first high-definition texture dictionary library of the first class.

If the dictionary library resource package does not include the first high-definition texture dictionary library of the first class, S1009 is executed, and the electronic device 100 may determine whether to download the first high-definition texture dictionary library according to the operation of the user. If the download is required, S1010 is executed, and the electronic device 100 sends the first category to the cloud server 200.

S1011, after receiving the first category, the cloud server 200 may determine that the shooting target category is the first high-definition texture dictionary library of the first category.

S1012 the cloud server 200 may send the first high definition texture dictionary library to the electronic device 100.

In one possible implementation, if the electronic device 100 determines that the first high-definition texture dictionary library is not downloaded according to the operation of the user, the electronic device 100 may perform super-resolution reconstruction on the low-definition image by using a general super-resolution processing policy, so as to obtain the high-definition image. The general super processing strategy comprises the steps of inputting a low-definition image into a super network model for super reconstruction, and obtaining a high-definition image. The super-resolution network model includes a super-resolution reconstruction generation type countermeasure network (SRGAN), an enhanced super-resolution reconstruction generation type countermeasure network (ESRGAN), a widely activated high-efficiency accurate image super-resolution reconstruction (WDSR) network, and the like.

In one possible implementation, if the dictionary library resource package does not include the first high-definition texture dictionary library of the first class, the electronic device 100 may skip step S1009 and directly execute step S1010.

If the dictionary library resource package includes the first high-definition texture dictionary library of the first class, the electronic device 100 may directly execute S1013.

S1013, after the electronic device 100 obtains the first high-definition texture dictionary library, a first region where the specified target content is located may be cut out from the low-definition image, so as to obtain a first low-definition cut image.

S1014, the electronic device 100 may extract the first image feature of the first low definition clip image.

For details, reference may be made to step S708 of the embodiment shown in fig. 7, which is not described herein.

S1015, the electronic device 100 may match a first high-definition texture feature similar to the first image feature from the first high-definition texture dictionary library.

For details, reference may be made to the processing procedure of the cloud server 200 in step S711 in the embodiment shown in fig. 7, which is not described herein.

S1016, the electronic device 100 may fuse the first high-definition texture feature into the first low-definition cropped image, to obtain the first high-definition cropped image.

For details, reference may be made to step S713 of the embodiment shown in fig. 7, which is not described herein.

S1017, the electronic device 100 may replace the first low-definition cropping image with the first high-definition cropping image, and paste the first high-definition cropping image back to the first area in the low-definition image to obtain the high-definition image.

S1018, the electronic device 100 saves the high-definition image.

For details, reference may be made to step S715 of the embodiment shown in fig. 7, which is not described herein.

Through the image reconstruction method provided by the embodiment of the application, the electronic device 100 may download the offline dictionary library resource package from the cloud server 200 in advance. The electronic device 100 may utilize the image features of the specified target content in the preview image captured by the camera before photographing to match the high-definition texture features similar to the image features of the specified target content from the high-definition texture dictionary library of the same class as the specified target content in the offline dictionary library resource package. When photographing, the electronic equipment can fuse the high-definition texture features into the photographed low-definition image to obtain the high-definition image. In this way, the target content in the preview image can be used to obtain the high-definition texture features in advance and then fused into the photographed low-definition image, so that the resolution of the photograph taken by the electronic device 100 is improved, and the time in the super-resolution reconstruction process is shortened.

Fig. 11 shows a flowchart of an image reconstruction method according to an embodiment of the present application. As shown in fig. 11, the method may include the steps of:

s1101, a plurality of high-definition texture dictionary libraries of different shooting target categories may be stored on the electronic device 100.

S1102, the electronic device 100 receives a first input.

S1103, the electronic device 100 acquires a low-definition image in response to the first input.

S1104, the electronic device 100 determines whether or not the specified target content is included in the low-definition image.

If the low-definition image includes the specified target content, S1105 is executed, where the electronic device 100 determines that the category of the specified target content in the low-definition image is the first category.

S1106, the electronic device 100 determines a first high-definition texture dictionary library with the shooting target category being a first category from high-definition texture dictionary libraries with a plurality of different shooting target categories.

S1107, the electronic device 100 can cut out a first area where the specified target content is located from the low-definition image to obtain a first low-definition cut-out image.

S1108, the electronic device 100 may extract the first image feature of the first low-definition clip image.

S1109, the electronic device 100 matches the first high-definition texture feature similar to the first image feature from the first high-definition texture dictionary library.

For details, reference may be made to the process of the cloud server 200 in step S711 of the embodiment shown in fig. 7 to match the first high-definition texture feature similar to the first image feature from the first high-definition texture dictionary library, which is not described herein.

S1110, the electronic device 100 may fuse the first high-definition texture feature into the first clipping image to obtain the first high-definition clipping image.

S1111, the electronic device 100 may replace the first low-definition clipping image with the first high-definition clipping image, and paste the first high-definition clipping image back to the first area in the low-definition image, to obtain the high-definition image.

When the low-definition image does not include the specified target content, the electronic device 100 may execute step S1112, and the electronic device 100 processes the low-definition image through the general super-processing policy to obtain a high-definition image.

The general super processing strategy comprises the steps of inputting a low-definition image into a super network model for super reconstruction, and obtaining a high-definition image. The super-resolution network model includes a super-resolution reconstruction generation type countermeasure network (SRGAN), an enhanced super-resolution reconstruction generation type countermeasure network (ESRGAN), a widely activated high-efficiency accurate image super-resolution reconstruction (WDSR) network, and the like.

S1013, the electronic apparatus 100 saves the high-definition image.

According to the image reconstruction method provided by the embodiment of the application, the electronic device 100 can store a plurality of high-definition texture dictionary libraries of different shooting target categories. The electronic device 100 may utilize the image features of the specified target content in the preview image captured by the camera before photographing to match the high-definition texture features similar to the image features of the specified target content from the high-definition texture dictionary libraries of the same class as the specified target content in the high-definition texture dictionary libraries of the multiple different photographing target classes. When photographing, the electronic equipment can fuse the high-definition texture features into the photographed low-definition image to obtain the high-definition image. In this way, the target content in the preview image can be used to obtain the high-definition texture features in advance and then fused into the photographed low-definition image, so that the resolution of the photograph taken by the electronic device 100 is improved, and the time in the super-resolution reconstruction process is shortened.

An image reconstruction system 1200 according to an embodiment of the present application is described below.

Fig. 12 shows a schematic architecture diagram of an image reconstruction system 1200 provided in an embodiment of the present application. The image reconstruction system 1200 may include, among other things, an electronic device 100 and a cloud server 200.

As shown in fig. 12, the electronic device 100 may include a camera 1211, an Image Signal Processor (ISP) 1212, a Digital Signal Processor (DSP) 1213, a target recognition module 1214, a feature extraction module 1215, a feature fusion module 1216, and an image stitching module 1217. Among other things, in some embodiments, the object recognition module 1214, feature extraction module 1215, feature fusion module 1216, and image stitching module 1217 may be a neural network computing processor (NPU) or a Graphics Processor (GPU). In other embodiments, the object recognition module 1214, feature extraction module 1215, feature fusion module 1216, and image stitching module 1217 may also be software processing modules in the application processor 1215.

The cloud server 200 may include a plurality of high-definition texture dictionary libraries of different shooting target types, a dictionary library matching module 1221 and a feature matching module 1222.

The camera 1211 may be used to capture an optical signal, convert the optical signal into an electrical signal, and send the electrical signal to the image signal processor 1212 when a camera application or function is started.

The image signal processor 912 is operable to convert an electrical signal transmitted from the camera 1211 into a digital image signal and transmit the digital image signal to the digital signal processor 1213.

The digital signal processor 1213 may be used to process the digital image signal into a preview image stream in a specified image format, which may be in Raw format, YUV format, RGB format, or the like.

The object recognition module 1214 may be configured to acquire a captured low-definition image from the signal processor 1213 in the book based on a shooting instruction generated by user operation trigger, and recognize the specified object content of the first category. The object recognition module 1214 may also recognize a first region where the specified object content is located in the low-definition image, generating a binary mask image. The object recognition module 1214 can recognize and crop out a first region in the low definition image to obtain a first low definition cropped image.

The feature extraction module 1215 may extract first image features from the first low definition cropped image.

The electronic device 100 may send the first category identified by the target identification module 1214 to the cloud server 200. The electronic device 100 may send the first image feature (or the first low definition clip image) to the cloud server 200. Wherein:

the dictionary library matching module 1221 may select a first high-definition texture dictionary library of the first class from a plurality of high-definition texture dictionary libraries of different shooting target classes to the feature matching module 1222.

The feature matching module 1222 may be used to match out a first high definition texture feature from a first high definition texture dictionary library that is similar to the first image feature. Optionally, the feature matching module 1222 may fuse the first high definition texture feature into the first low definition cropped image to obtain the first high definition cropped image after identifying the first high definition texture feature with similar first image features.

The cloud server 200 may send the first high definition texture feature (or the first high definition cropped image) to the electronic device 100. Wherein:

the feature fusion module 1216 may fuse the first high definition texture feature into the first low definition cropped image, resulting in the first high definition cropped image.

The image stitching module 1217 may use the binary mask image to obtain a first region in the low-definition image where the specified target content is located, and paste the first high-definition cropped image back to the first region in the low-definition image to obtain the high-definition image.

For the parts not described in detail, reference may be made to the foregoing embodiments, and details are not repeated here.

The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An image reconstruction method, comprising:

the electronic equipment displays a shooting preview interface, wherein a shooting key and a preview image stream acquired by a camera in real time are displayed on the shooting preview interface;

the electronic equipment detects that the first preview image comprises second specified target content, wherein the category of the second specified target content is a second category which is the same as or different from the first category;

the electronic equipment extracts second image features in the first preview image;

the electronic equipment sends the second image characteristics and the second category to a cloud server; the cloud server is used for matching a second high-definition texture dictionary library with a shooting target class as a second class from a plurality of stored high-definition texture dictionary libraries with different shooting target classes; the second image features are used for matching second high-definition texture features, the similarity of which with the second image features is larger than a preset value, from the second high-definition texture dictionary library by the cloud server;

the electronic equipment receives the second high-definition texture feature sent by the cloud server;

The electronic equipment stores the second high-definition texture features into a cache queue;

the electronic device receives a first input for the photographing key;

the electronic equipment responds to the first input to acquire a low-definition image acquired by the camera;

the electronic equipment detects that the low-definition image comprises first specified target content, and the category of the first specified target content is a first category;

the electronic equipment extracts first image characteristics of the first appointed target content in the low-definition image;

if the cache queue does not store the high-definition texture features with the similarity to the first image features being larger than a preset value, the electronic equipment sends the first image features and the first categories to a cloud server; the cloud server is used for matching a first high-definition texture dictionary library with a shooting target class as a first class from a plurality of stored high-definition texture dictionary libraries with different shooting target classes; the cloud server is used for matching first high-definition texture features, the similarity of which with the first image features is larger than a preset value, from the first high-definition texture dictionary library;

The electronic equipment receives the first high-definition texture feature sent by the cloud server;

and the electronic equipment fuses the first high-definition texture features into the low-definition image to obtain a high-definition image, wherein the resolution of the first specified target content in the high-definition image is larger than that of the first specified target content in the low-definition image.

2. The method according to claim 1, wherein the electronic device extracts a first image feature of the first specified target content in the low-definition image, specifically comprising:

the electronic equipment identifies and cuts out a first area where the first appointed target content is located in the low-definition image to obtain a first low-definition cut-out image, wherein the first low-definition cut-out image comprises the first appointed target content;

the electronic device extracts a first image feature of the first low definition cropped image.

3. The method according to claim 2, wherein the electronic device fuses the first high-definition texture bits into the low-definition image to obtain a high-definition image, specifically comprising:

the electronic equipment fuses the first high-definition texture features into the first low-definition clipping image to obtain a first high-definition clipping image;

And the electronic equipment replaces the first high-definition clipping image with the first low-definition clipping image, and pastes the first high-definition clipping image back to the first area in the low-definition image to obtain the high-definition image.

4. A method according to any of claims 1-3, wherein after the electronic device fuses the first high definition texture feature into the low definition image, the method further comprises:

and the electronic equipment stores the high-definition image.

5. A method according to any one of claims 1-3, wherein a first control is also displayed on the shooting preview interface; before the electronic device receives the first input, the method further comprises:

the electronic device receives a second input for the first control;

in response to the second input, the electronic device turns on a superdivision reconstruction mode.

6. A method according to any of claims 1-3, wherein prior to the electronic device receiving the first input, the method further comprises:

the electronic equipment displays a picture browsing interface, the picture browsing interface is displayed with the low-definition image and the second control, and the first input is input aiming at the second control.

7. A method according to any of claims 1-3, wherein the electronic device extracts a second image feature of the second specified target content in the first preview image, comprising in particular:

the electronic equipment identifies and cuts out a second area where the second specified target content is located in the low-definition image to obtain a second low-definition cut image;

the electronic device extracts a second image feature of the second low definition cropped image.

8. The method of any of claims 1-3, wherein after the electronic device detects that a second specified target content is included in the first preview image, the method further comprises:

and the electronic equipment displays category information on the shooting preview interface, wherein the category information is used for indicating that the category of the appointed target content in the preview image stream is the second category.

9. The method of any of claims 1-3, wherein after the electronic device stores the second high definition texture feature in a cache queue, the method further comprises;

the electronic equipment acquires a second preview image acquired by the camera;

The electronic device detects that the second preview image comprises third specified target content, wherein the class of the third specified target content is a third class which is the same as or different from the second class;

the electronic equipment identifies and cuts out a third area where the third appointed target content is located in the low-definition image to obtain a third low-definition cut-out image;

the electronic equipment extracts a third image feature of the third low definition clipping image;

the electronic equipment judges whether high-definition texture features with similarity with the third image features larger than a preset value are stored in the cache queue;

if the cache queue stores high-definition texture features with the similarity to the third image features being larger than a preset value, the electronic equipment fuses the high-definition texture features with the similarity to the third image features being larger than the preset value in the cache queue into the third low-definition clipping image to obtain a third high-definition clipping image;

the electronic equipment replaces the third high-definition clipping image with the third low-definition clipping image, and pastes the third high-definition clipping image back to a third area in the second preview image to obtain a high-definition preview image;

And the electronic equipment displays the high-definition preview image on the shooting preview interface.

10. The method of claim 9, wherein if the cache queue does not store high definition texture features having a similarity to the third image feature greater than a predetermined value, the method further comprises:

the electronic equipment sends the third image feature and the third category to a cloud server; the cloud server is used for matching a third high-definition texture dictionary library with a shooting target class as a third class from a plurality of stored high-definition texture dictionary libraries with different shooting target classes; the cloud server is used for matching a third high-definition texture feature with similarity larger than a preset value with the third image feature from the third high-definition texture dictionary library;

the electronic equipment receives the third high-definition texture feature sent by the cloud server;

and the electronic equipment stores the third high-definition texture features into the cache queue, and fuses the third high-definition texture features into the third low-definition cropping image to obtain the third high-definition cropping image.

11. The method of claim 1, wherein if the cache queue stores high definition texture features having a similarity to the first image feature greater than a preset value, the method further comprises:

and the electronic equipment fuses the high-definition texture features, the similarity of which with the first image features is larger than a preset value, in the cache queue into the low-definition image to obtain the high-definition image.

12. An electronic device, comprising: one or more processors, one or more memories, and a display screen; the one or more memories coupled with one or more processors, the one or more memories for storing computer program code comprising computer instructions that, when executed by the one or more processors, cause the electronic device to perform the image reconstruction method of any of the above claims 1-11.

13. A readable storage medium storing computer instructions which, when run on an electronic device, cause the electronic device to perform the image reconstruction method of any one of the preceding claims 1-11.

14. The image reconstruction system is characterized by comprising electronic equipment and a cloud server, wherein the cloud server is stored with a plurality of high-definition texture dictionary libraries of different shooting target categories; wherein,,

the electronic equipment is used for displaying a shooting preview interface, and a shooting key and a preview image stream acquired by a camera in real time are displayed on the shooting preview interface;

the electronic device is further configured to detect that the first preview image includes second specified target content, where a category of the second specified target content is a second category, and the second category is the same as or different from the first category;

the electronic equipment is also used for extracting second image features in the first preview image;

the electronic device is further configured to send the second image feature and the second class to a cloud server; the cloud server is used for matching a second high-definition texture dictionary library with a shooting target class as a second class from a plurality of stored high-definition texture dictionary libraries with different shooting target classes; the second image features are used for matching second high-definition texture features, the similarity of which with the second image features is larger than a preset value, from the second high-definition texture dictionary library by the cloud server;

The electronic equipment is further used for receiving the second high-definition texture features sent by the cloud server;

the electronic equipment is further used for storing the second high-definition texture features into a cache queue;

the electronic device is further configured to receive a first input for the capture key;

the electronic equipment is further used for responding to the first input and acquiring a low-definition image acquired by the camera;

the electronic device is further configured to detect that the low-definition image includes a first specified target content, where a category of the first specified target content is a first category;

the electronic equipment is also used for extracting first image features of the first appointed target content in the low-definition image;

the electronic device is further configured to send the first image feature and the first class to the cloud server if the cache queue does not store high-definition texture features having a similarity with the first image feature greater than a preset value;

the cloud server is used for matching a first high-definition texture dictionary library with a shooting target class being a first class from the stored high-definition texture dictionary libraries with a plurality of different shooting target classes;

The cloud server is further configured to match a first high-definition texture feature with a similarity greater than a preset value with the first image feature from the first high-definition texture dictionary library;

the cloud server is further configured to send the first high-definition texture feature to the electronic device;

the electronic device is further configured to fuse the first high-definition texture feature into the low-definition image to obtain a high-definition image, where a resolution of the first specified target content in the high-definition image is greater than a resolution of the first specified target content in the low-definition image.

15. The system of claim 14, wherein the cloud server is further configured to obtain a plurality of high quality images and a photographic target category for each high quality image;

the cloud server is also used for extracting high-definition texture features of shooting target categories in the high-quality images and storing the high-definition texture features in a high-definition texture dictionary library corresponding to the shooting target categories of the high-quality images.

16. The system of claim 14 or 15, wherein the electronic device is further configured to send a set of identification categories of a target identification model on the electronic device to the cloud server;

The cloud server is further configured to send model update information to the electronic device when it is determined that the identification class set is different from class sets of the high-definition texture dictionary libraries of the plurality of different shooting target classes;

the electronic equipment is further used for updating a target identification model on the electronic equipment based on the model updating information; after updating, the identification class set of the target identification model on the electronic equipment is the same as the class set of the high-definition texture dictionary library of the plurality of different shooting target classes.