CN113538226A

CN113538226A - Image texture enhancement method, device, equipment and computer readable storage medium

Info

Publication number: CN113538226A
Application number: CN202010311962.4A
Authority: CN
Inventors: 武小宇; 秦超; 张运超
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-04-20
Filing date: 2020-04-20
Publication date: 2021-10-22
Anticipated expiration: 2040-04-20
Also published as: CN113538226B

Abstract

The invention provides a hyperspectral image texture enhancement method, a hyperspectral image texture enhancement device, hyperspectral image texture enhancement equipment and a computer readable storage medium, wherein the hyperspectral image texture enhancement method comprises the following steps: acquiring a visible light image of a shot object through a visible light sensor of the terminal device, and acquiring a hyperspectral image of the shot object through a hyperspectral sensor of the terminal device, wherein the visible light image and the hyperspectral image are obtained through the same exposure; performing texture enhancement processing on the hyperspectral image to obtain a texture enhanced hyperspectral image; and fusing the texture enhanced hyperspectral image and the visible light image to obtain a texture enhanced image. According to the image texture enhancement method, the hyperspectral image is used for texture enhancement, more texture details can be obtained, and the problems that texture features of different areas are different and imaging texture details are not ideal in the traditional deep learning method are solved.

Description

Image texture enhancement method, device, equipment and computer readable storage medium

Technical Field

The present invention relates to image processing technologies, and in particular, to an image texture enhancement method and apparatus, an electronic device, and a computer-readable storage medium.

Background

Photographing is a widely used function in mobile phones. With the continuous development of the technology, there is an increasing demand for clear presentation of texture details (skin, hair, clothes, etc.) in some specific areas of the mobile phone camera during imaging.

Ignatov, Andrey, and the like (Ignatov, Andrey, et al, "DSLR-quality on mobile devices with depth dependent networks," Proceedings of the IEEE International Conference on Computer Vision.2017 ") propose that a CNN-based deep learning method performs full scene texture detail enhancement in a visible light range, but the scheme depends heavily on data, and training data is a full scene image, and the difference of texture details in different regions is large, so that a network cannot fully learn the texture features of the different regions, and the effect is improved. In addition, the training data is an RGB three-channel image in a visible light frequency band, and the provided detail information is limited.

Lai, Chao et al (Lai, Chao, et al, "Image super-resolution based segmentation and classification with specificity," 20162 nd IEEE International reference Computer and Communications (ICCC). IEEE,2016) propose to first segment regions of different objects and then enhance the texture details of the particular regions after segmentation. However, the traditional segmentation method is based on clustering/SVM and the like, on one hand, the traditional segmentation method can only segment the regions with the same color in the RGB domain, and cannot distinguish different objects and materials; on the other hand, the method based on deep learning heavily depends on a large amount of data of artificial pixel-level labeling, the labeling and calculation are time-consuming, and objects with different materials and colors in the same object are difficult to distinguish.

Disclosure of Invention

In view of the above, the present invention provides a hyperspectral image texture enhancement method, an enhancement apparatus, an electronic device, and a computer-readable storage medium, where the hyperspectral image texture enhancement method can draw out an area of a specified material (e.g., skin, hair, clothing, etc.) using a hyperspectral image and perform texture enhancement processing on the specified material area in the hyperspectral image, so as to effectively solve the problem that texture detail information is not ideal due to the fact that a mapping relationship of texture enhancement cannot be fully learned by a conventional deep learning method because the texture features of areas of different materials are different, and can obtain a better texture detail enhancement effect for the specified area, thereby improving the imaging quality of a photo.

The present application is described below in terms of several aspects, embodiments and advantages of which are mutually referenced.

In a first aspect, the present application provides a hyperspectral-based image texture enhancement method, used for a terminal device, the method including: acquiring a visible light image of a shot object through a visible light sensor of the terminal device, and acquiring a hyperspectral image of the shot object through a hyperspectral sensor of the terminal device, wherein the visible light image and the hyperspectral image are obtained through the same exposure; performing texture enhancement processing on the hyperspectral image to obtain a texture enhanced hyperspectral image; and fusing the texture enhanced hyperspectral image and the visible light image to obtain a texture enhanced image.

According to the embodiment of the application, when a terminal device (for example, a mobile terminal device such as a mobile phone or an IPAD) is used for photographing, a visible light image and a hyperspectral image are simultaneously acquired through a visible light sensor and a hyperspectral sensor in the terminal device by one-time exposure respectively, then, the hyperspectral image is used for texture enhancement processing, and then the texture enhanced hyperspectral image and the visible light image are fused, so that a final texture enhanced image can be obtained. The hyperspectral image can obtain more texture details than the visible light image. The method solves the problem that the imaging texture details are not ideal due to the fact that the traditional deep learning method cannot fully learn the mapping relation of texture enhancement because the texture features of different regions are different.

In a possible implementation of the first aspect, the performing texture enhancement processing on the hyperspectral image includes: extracting texture features from the hyperspectral image; for the hyperspectral image, performing material segmentation according to spectral information to divide the hyperspectral image into a plurality of regions; calibrating the hyperspectral image of the area needing texture enhancement based on the spectral information of the hyperspectral image through a classification network model to obtain a mask; and performing texture detail enhancement processing on the region needing texture enhancement based on the mask and the texture features to obtain the texture enhanced hyperspectral image. That is, firstly, extracting the texture features of a hyperspectral image to form a texture feature image; meanwhile, for the hyperspectral image, material segmentation is carried out on the basis of spectral information of the hyperspectral image so as to divide the hyperspectral image into a plurality of regions, the same/similar material is arranged in each region, the material is different between different regions, and then the regions needing texture enhancement are calibrated by a classification network model for different regions so as to obtain masks; and finally, performing texture detail enhancement processing based on the mask and texture features extracted from the hyperspectral image.

Further, for the hyperspectral image, the texture features can be extracted through a kernel principal component analysis method. That is to say, for the hyperspectral image, the texture features of the hyperspectral image are extracted through a kernel principal component analysis algorithm to obtain a texture feature image.

In one possible implementation of the first aspect, the area requiring texture enhancement includes one or more of a hair area, a skin area, a metal area, a resin/glass area, and a fiber area. Of course, the above description is given by way of example only and is not intended to limit the scope of the present invention. When texture enhancement processing is performed on an imaged image in a scene for taking a portrait photo by a mobile phone, the texture enhancement processing may be performed on a designated area (which is also a region that needs to be texture enhanced) instead of a full image.

In a possible implementation of the first aspect, the classification network model is formed by: acquiring a plurality of samples of different materials corresponding to different areas of a shooting object; for each sample, acquiring spectral information and corresponding calibration information of the sample; and training by adopting a convolutional neural network based on the sample to obtain the classification network model. Firstly, obtaining a plurality of samples with different skin colors and the like, hair/hair quality samples with different colors, glasses samples with different materials, cloth samples with different materials and the like; calibrating each sample, and acquiring spectral information of the sample; and training the target function by adopting a convolutional neural network based on the sample to obtain a classification network model. Different samples can be adopted to obtain different classification network models corresponding to different shooting objects.

In a possible implementation of the first aspect, performing the texture detail enhancement on the region needing texture enhancement based on the mask and the texture feature includes: and for the area needing texture enhancement, combining the mask and the texture features, and inputting the mask and the texture features into a texture enhancement network (such as a convolutional neural network) for fusion so as to realize the texture detail enhancement processing. That is, for the region needing texture enhancement (for example, human face, etc.), the mask obtained by calibration of the classification network model is multiplied by the texture features, so that the texture enhanced hyperspectral image can be obtained.

In another possible implementation of the first aspect, the performing enhancement processing on the hyperspectral image includes: obtaining images of all channels based on the hyperspectral images; performing super-resolution processing on the basis of the channel images through a super-resolution network to obtain high-resolution images of the channels; and fusing the high-resolution images of the channels to obtain the texture enhanced hyperspectral image, wherein the texture enhanced hyperspectral image is a high-resolution multichannel hyperspectral image. Different from the above implementation, in the implementation, the hyperspectral image is not subjected to material segmentation based on the spectral information, but the full image enters the super-resolution network, so that the texture enhancement of the low-resolution image is realized.

In one possible implementation of the first aspect, the super-resolution network includes a feature extraction module, an upsampling module, and a convolutional layer, and performing the super-resolution processing based on each channel image through the super-resolution network includes: extracting texture features of the images of each channel through a feature extraction module to obtain texture feature images of each channel; based on the texture feature images of each channel, improving the spatial resolution through an up-sampling module; and reconstructing the texture characteristic image output by the up-sampling module through the convolution layer. That is, for each channel image, extracting texture features through a feature extraction module in a super-resolution network, thereby obtaining texture feature images of each channel; then, inputting the texture feature images of all channels into an up-sampling module to improve the spatial resolution; and finally, reconstructing the texture feature image with the improved resolution output by the up-sampling module through the convolution layer, thereby restoring a readable high-resolution image of each channel. According to the implementation manner of the application, high-resolution processing is performed on each channel image based on the hyperspectral image, and compared with the method of directly performing super-resolution processing by using a visible light image, more details can be acquired through the channel images of dozens of channels of the hyperspectral image, and the texture enhancement effect is better.

In a possible implementation of the first aspect, the feature extraction module includes any one of a residual block, a dense block, a cluster block, and a residual cluster block. Of course, the feature extraction module is not limited to the above, and may be any module capable of performing feature extraction.

In a possible implementation of the first aspect, the upsampling module comprises any one of interpolated upsampling, deconvolution upsampling, sub-pixel convolution upsampling, and clique upsampling. Of course, the up-sampling module may not be limited thereto. The image reconstruction performance can be further improved by adopting a proper up-sampling module. Through the up-sampling module, the spatial resolution of the input texture feature images of all channels can be improved.

In one possible implementation of the first aspect, in the convolutional layer, the spectral constraint term is constructed by using pixels with spectral similarity within a predetermined threshold range. That is to say, when the high-resolution characteristic images of each channel are restored by the convolution layer to obtain high resolution, the spectral constraint term is constructed by using the pixels with the spectral similarity within the predetermined threshold range, so that the accuracy of the reconstructed image spectrum can be improved.

In a second aspect, the present application provides an image texture enhancing apparatus, for a terminal device, including: the image acquisition module is used for acquiring a visible light image and a hyperspectral image of a shot object, wherein the visible light image and the hyperspectral image are obtained through the same exposure; the enhancement module is used for enhancing the hyperspectral image to obtain an enhanced hyperspectral image; and the fusion module is used for fusing the enhanced hyperspectral image with the visible light image to obtain a texture enhanced image.

In a possible implementation of the second aspect, the enhancing module may include:

the texture feature extraction module is used for extracting texture features from the hyperspectral image;

the segmentation module is used for performing material segmentation on the hyperspectral image according to spectral information so as to divide the hyperspectral image into a plurality of areas;

the classification network module is used for calibrating the hyperspectral image of the area needing texture enhancement based on the spectral information of the hyperspectral image to obtain a mask;

and the texture detail processing module is used for performing texture detail enhancement processing on the region needing texture enhancement based on the mask and the texture features to obtain the texture enhancement hyperspectral image.

In a possible implementation of the second aspect, the texture feature extraction module is configured to extract the texture feature through kernel principal component analysis.

In one possible implementation of the second aspect, the area requiring texture enhancement comprises one or more of a hair area, a skin area, a metal area, a resin/glass area, a fiber area.

In one possible implementation of the second aspect, the classification network module is formed by:

acquiring a plurality of samples of different materials corresponding to different areas of a shooting object;

for each sample, acquiring spectral information and corresponding calibration information of the sample;

and training by adopting a convolutional neural network based on the sample to obtain the classification network module.

In one possible implementation of the second aspect, the texture detail processing module includes:

a combining module for combining the mask with the texture features;

and the texture enhancement submodule is used for fusing the input from the combination module to realize the texture detail enhancement processing, so that a hyperspectral texture enhanced image is obtained.

In another possible implementation of the second aspect, the enhancing module includes:

the channel image acquisition module is used for acquiring each channel image based on the hyperspectral image;

the super-resolution network module is used for carrying out super-resolution processing on the basis of the images of all the channels to obtain high-resolution images of all the channels;

and the reconstruction module is used for fusing the high-resolution images of the channels to obtain the texture enhanced hyperspectral image, and the texture enhanced hyperspectral image is a high-resolution multichannel hyperspectral image.

In one possible implementation of the second aspect, the super-resolution network module includes: a feature extraction module, an upsampling module, and a convolutional layer,

the characteristic extraction module is used for extracting texture characteristics from the images of all channels to obtain texture characteristic images of all channels;

the upper adoption module is used for improving the spatial resolution based on the texture feature images of all the channels;

and the convolution layer is used for reconstructing the texture characteristic image output by the up-sampling module.

In one possible implementation of the second aspect, the feature extraction module includes any one of a residual block, a dense block, a cluster block, and a residual cluster block.

In a possible implementation of the second aspect, the upsampling module comprises any one of interpolated upsampling, deconvolved upsampling, sub-pixel convolved upsampling, and clique upsampling.

In one possible implementation of the second aspect, in the convolutional layer, the spectral constraint term is constructed using pixels whose spectral similarity is within a predetermined threshold range.

In a third aspect, the present application provides an electronic device, comprising: one or more processors; one or more memories having computer readable code stored therein, which when executed by the one or more processors, causes the processors to perform the image texture enhancement method of any one of the possible implementations of the first aspect of the application described above.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer-readable code which, when executed by one or more processors, causes the processors to perform the image texture enhancement method of any one of the possible implementations of the first aspect of the present application described above.

Drawings

FIG. 1 is a diagram of an application scenario of an image texture enhancement process provided according to an embodiment of the present application;

FIG. 2 is a schematic structural diagram of an electronic device provided in accordance with an embodiment of the present application;

FIG. 3 is a block diagram of a software architecture of an electronic device provided according to an embodiment of the present application;

FIG. 4 is a flowchart illustrating an image texture enhancement method according to an embodiment of the present application;

fig. 5 is a schematic flowchart of performing texture enhancement processing on a hyperspectral image in an image texture enhancement method according to an embodiment of the application;

FIG. 6 is a schematic flow chart of an image texture enhancement method according to an embodiment of the present application;

FIG. 7 is an image processed in each step of the image texture enhancement method shown in FIG. 6, where (a) is a visible light image, (b) is a hyperspectral image, (c) is a texture feature image, (d) is a texture-enhanced hyperspectral image, and (e) is a final texture-enhanced image;

fig. 8 is a schematic flowchart of performing texture enhancement processing on a hyperspectral image in an image texture enhancement method according to another embodiment of the present application;

FIG. 9 is a flowchart illustrating super-resolution processing according to the image texture enhancement method shown in FIG. 8;

FIG. 10 is a block diagram of an image texture enhancement apparatus provided according to one embodiment of the present application;

FIG. 11 is a block diagram of an enhancement module for use in the image texture enhancement method provided in accordance with the embodiment of FIG. 5;

FIG. 12 is a block diagram of an enhancement module for use in the image texture enhancement method provided in accordance with the embodiment of FIG. 8;

FIG. 13 is a block diagram of an apparatus according to some embodiments of the present application;

fig. 14 is a block diagram of a system on a chip (SoC) according to some embodiments of the present application.

Detailed Description

The technical solution in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It will be appreciated that as used herein, the term module may refer to or include an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and/or memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable hardware components that provide the described functionality, or may be part of such hardware components.

It will be appreciated that in the various embodiments of the present application, the processor may be a microprocessor, a digital signal processor, a microcontroller, the like, and/or any combination thereof. According to another aspect, the processor may be a single-core processor, a multi-core processor, the like, and/or any combination thereof.

Hereinafter, embodiments of the present application will be described in further detail with reference to the accompanying drawings.

Fig. 1 is a view of an application scene of an image texture enhancement processing method according to an embodiment of the present application. Fig. 1 shows a scene in which a terminal device (fig. 1 shows a case where the terminal device is a mobile phone) is used to perform self-timer shooting or shooting of a shooting object. Under the condition, when the shutter is pressed (or the shooting key is triggered), the visible light image and the hyperspectral image are simultaneously obtained through the visible light sensor and the hyperspectral sensor in the mobile phone, then the mobile phone performs texture enhancement processing by utilizing the hyperspectral image, fuses the texture enhanced hyperspectral image and the visible light image which are obtained after the texture enhancement processing, and finally outputs the texture enhanced image. That is to say, after the mobile phone triggers the shooting function, the processor in the mobile phone calls the image enhancement processing method of the application to process, and finally the texture enhanced image is output.

FIG. 2 illustrates a schematic structural diagram of an electronic device 100 according to some embodiments of the present application.

The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a Universal Serial Bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a key 190, a motor 191, an indicator 192, a camera 193, a display screen 194, a Subscriber Identification Module (SIM) card interface 195, and the like. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light 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 is to be understood that the illustrated structure of the embodiment of the present invention does not specifically limit the electronic device 100. In other embodiments of the present application, electronic device 100 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Processor 110 may include one or more processing units, such as: the processor 110 may include an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a video codec, a Digital Signal Processor (DSP), a baseband processor, and/or a neural-Network Processing Unit (NPU), etc. The different processing units may be separate devices or may be integrated into one or more processors.

The processor 110 may generate operation control signals according to the instruction operation code and the timing signals, so as to complete the control of instruction fetching and instruction execution.

A memory may also be provided in 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 have just been used or recycled by the processor 110. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. Avoiding repeated accesses reduces the latency of the processor 110, thereby increasing the efficiency of the system.

In some embodiments, processor 110 may include one or more interfaces. The interface may include an integrated circuit (I2C) interface, an integrated circuit built-in audio (I2S) interface, a Pulse Code Modulation (PCM) interface, a universal asynchronous receiver/transmitter (UART) interface, a Mobile Industry Processor Interface (MIPI), a general-purpose input/output (GPIO) interface, a Subscriber Identity Module (SIM) interface, and/or a Universal Serial Bus (USB) interface, etc.

The I2C interface is a bi-directional synchronous serial bus that includes a serial data line (SDA) and a Serial Clock Line (SCL). In some embodiments, processor 110 may include multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, the charger, the flash, the camera 193, etc. through different I2C bus interfaces, respectively. For example: the processor 110 may be coupled to the touch sensor 180K via an I2C interface, such that the processor 110 and the touch sensor 180K communicate via an I2C bus interface to implement the touch functionality of the electronic device 100.

The I2S interface may be used for audio communication. In some embodiments, processor 110 may include 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 communicate audio signals to the wireless communication module 160 via the I2S interface, enabling answering of calls via a bluetooth headset.

The PCM interface may also be used for audio communication, sampling, quantizing and encoding analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled by 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, so as to implement a function of answering a call through a 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 used for asynchronous communications. The bus may be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is generally 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 the audio signal to the wireless communication module 160 through a UART interface, so as to realize the function of playing music through a bluetooth headset.

MIPI interfaces may be used to connect processor 110 with peripheral devices such as display screen 194, camera 193, and the like. The MIPI interface includes a Camera Serial Interface (CSI), a Display Serial Interface (DSI), and the like. In some embodiments, processor 110 and camera 193 communicate through a CSI interface to implement the capture functionality of electronic device 100. The processor 110 and the display screen 194 communicate through the DSI interface to implement the display function of the electronic device 100.

The GPIO interface may be configured by software. The GPIO interface may be configured as a control signal and may also be configured 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, a MIPI interface, and the like.

It should be understood that the connection relationship between the modules according to the embodiment of the present invention is only illustrative, and is not limited to the structure of the electronic device 100. In other embodiments of the present application, the electronic device 100 may also adopt different interface connection manners or a combination of multiple interface connection manners in the above embodiments.

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 transmit data between the electronic device 100 and a peripheral device. And the earphone can also be used for connecting an earphone and playing audio through the earphone. The interface may also be used to connect other electronic devices, such as AR devices and the like.

The charging management module 140 is configured to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger via the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive a 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 to connect the battery 142, the charging 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 supplies power to the processor 110, the internal memory 121, the display 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be used to monitor parameters such as battery capacity, battery cycle count, battery state of health (leakage, impedance), etc. In some other embodiments, the power management module 141 may also be disposed in the processor 110. In other embodiments, the power management module 141 and the charging 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 can also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed as 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 including 2G/3G/4G/5G wireless communication applied to the electronic device 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a Low Noise Amplifier (LNA), and the like. The mobile communication module 150 may receive the electromagnetic wave from the antenna 1, filter, amplify, etc. the received electromagnetic wave, and transmit the electromagnetic wave to the modem processor for demodulation. The mobile communication module 150 may also amplify the signal modulated by the modem processor, and convert the signal into electromagnetic wave through the antenna 1 to radiate the electromagnetic wave. 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 disposed 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 a 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 passes the demodulated low frequency baseband signal to a 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 a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.) or displays an image 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 modules, independent of the processor 110.

The wireless communication module 160 may provide a solution for wireless communication applied to the electronic device 100, including Wireless Local Area Networks (WLANs) (e.g., wireless fidelity (Wi-Fi) networks), bluetooth (bluetooth, BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), Infrared (IR), and the like. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, performs frequency modulation and filtering processing on 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, perform frequency modulation and amplification on the signal, and convert the signal into electromagnetic waves through the antenna 2 to radiate the electromagnetic waves.

In some embodiments, antenna 1 of electronic device 100 is coupled to mobile communication module 150 and antenna 2 is coupled to wireless communication module 160 so that electronic device 100 can communicate with networks and other devices through wireless communication techniques. The wireless communication technology may include global system for mobile communications (GSM), General Packet Radio Service (GPRS), code division multiple access (code division multiple access, CDMA), Wideband Code Division Multiple Access (WCDMA), time-division code division multiple access (time-division code division multiple access, TD-SCDMA), Long Term Evolution (LTE), LTE, BT, GNSS, WLAN, NFC, FM, and/or IR technologies, etc. The GNSS may include a Global Positioning System (GPS), a global navigation satellite system (GLONASS), a beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS), and/or a Satellite Based Augmentation System (SBAS).

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

The display screen 194 is used to display images, video, and the like. The display screen 194 includes a display panel. The display panel may adopt a Liquid Crystal Display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (active-matrix organic light-emitting diode, AMOLED), a flexible light-emitting diode (FLED), a miniature, a Micro-oeld, a quantum dot light-emitting diode (QLED), and the like. In some embodiments, the electronic device 100 may include 1 or N display screens 194, with N being a positive integer greater than 1.

The electronic device 100 may implement a shooting function through the ISP, the camera 193, the video codec, the GPU, the display 194, the application processor, and the like.

The ISP is used to process the data fed back by the camera 193. For example, when a photo is taken, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing and converting into an image visible to naked eyes. The ISP can also carry out algorithm optimization on 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 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 to the photosensitive element. The photosensitive element may be a Charge Coupled Device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The light sensing element converts the optical signal into an electrical signal, which is then passed to the ISP where it is converted into a digital image signal. And the ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into image signal in standard RGB, YUV and other formats. In some embodiments, the 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 digital image signals and other digital signals. For example, when the electronic device 100 selects a frequency bin, the digital signal processor is used to perform fourier transform or the like on the frequency bin energy.

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: moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, and the like.

The NPU is a neural-network (NN) computing processor that processes input information quickly by using a biological neural network structure, for example, by using a transfer mode between neurons of a human brain, and can also learn by itself continuously. Applications such as intelligent recognition of the electronic device 100 can be realized through the NPU, for example: image recognition, face recognition, speech recognition, text understanding, and the like.

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

The internal memory 121 may be used to store computer-executable program code, which includes instructions. The internal memory 121 may include a program storage area and a data storage area. The storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required by at least one function, and the like. The storage data area may store data (such as audio data, phone book, etc.) created during use of the electronic device 100, and the like. 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 (UFS), and the like. The processor 110 executes various functional applications of the electronic device 100 and data processing by executing instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.

According to some embodiments of the present application, the internal memory 121 has instructions (in other words, computer readable codes) stored therein, and the processor 110 executes the image enhancement processing method according to the present application when reading the instructions stored in the internal memory 121. Specifically, reference may be made to the image enhancement processing method of the following embodiment.

The electronic device 100 may implement audio functions via the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone interface 170D, and the application processor. 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 some functional modules of the audio module 170 may be disposed in the processor 110.

The speaker 170A, also called a "horn", is used to convert the audio electrical signal into an acoustic signal. The electronic apparatus 100 can listen to music through the speaker 170A or listen to a handsfree call.

The receiver 170B, also called "earpiece", is used to convert the electrical audio signal into an acoustic signal. When the electronic apparatus 100 receives a call or voice information, it can receive voice by placing the receiver 170B close to the ear of the person.

The microphone 170C, also referred to as a "microphone," is used to convert sound signals into electrical signals. When making a call or transmitting voice information, the user can input a voice signal to the microphone 170C by speaking the user's mouth near 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 to achieve a noise reduction function in addition to collecting sound signals. In other embodiments, the electronic device 100 may further include three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, perform directional recording, and so on.

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

The pressure sensor 180A is used for sensing a pressure signal, and converting 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 can be of a wide variety, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a sensor comprising at least two parallel plates having an electrically conductive material. When a force acts on the pressure sensor 180A, the capacitance between the electrodes changes. 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 intensity of the touch operation according to the pressure sensor 180A. The electronic apparatus 100 may also calculate the touched position from the detection signal of the pressure sensor 180A. In some embodiments, the touch operations that are applied to the same touch position but different touch operation intensities may correspond to different operation instructions. For example: and when the touch operation with the touch operation intensity smaller than the first pressure threshold value acts on the short message application icon, executing an instruction for viewing the short message. And when the touch operation with the touch operation intensity larger than or equal to the first pressure threshold value acts on the short message application icon, executing an instruction of newly building the short message.

The gyro sensor 180B may be used to determine the motion attitude of the electronic device 100. In some embodiments, the angular velocity of electronic device 100 about three axes (i.e., the x, y, and z axes) may be determined by gyroscope 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 a shake angle of the electronic device 100, calculates a distance to be compensated for by the lens module according to the shake angle, and allows the lens to counteract the shake of the electronic device 100 through a reverse movement, thereby achieving anti-shake. The gyroscope sensor 180B may also be used for navigation, somatosensory gaming scenes.

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

The magnetic sensor 180D includes a hall sensor. The electronic device 100 may detect the opening and closing of the flip holster using the magnetic sensor 180D. In some embodiments, when the electronic device 100 is a flip phone, the electronic device 100 may detect the opening and closing of the flip according to the magnetic sensor 180D. And then according to the opening and closing state of the leather sheath or the opening and closing state of the flip cover, the automatic unlocking of the flip cover is 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 can be detected when the electronic device 100 is stationary. The method can also be used for recognizing the posture 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, taking a picture of a scene, electronic device 100 may utilize range sensor 180F to range for fast 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 to the outside 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 can be determined that there is an object near the electronic device 100. When insufficient reflected light is detected, the electronic device 100 may determine that there are no objects near the electronic device 100. The electronic device 100 can utilize the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear for talking, so as to automatically turn off the screen to achieve the purpose of saving power. The proximity light sensor 180G may also be used in a holster mode, a pocket mode automatically unlocks and locks the screen.

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

The fingerprint sensor 180H is used to collect a fingerprint. The electronic device 100 can utilize the collected fingerprint characteristics to unlock the fingerprint, access the application lock, photograph the fingerprint, answer an incoming call with the fingerprint, and so on.

The temperature sensor 180J is used to detect temperature. In some embodiments, electronic device 100 implements a temperature processing strategy using the temperature detected by temperature sensor 180J. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the electronic device 100 performs a reduction in performance of a processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection. In other embodiments, the electronic device 100 heats the battery 142 when the temperature is below another threshold to avoid the low temperature causing the electronic device 100 to shut down abnormally. In other embodiments, when the temperature is lower than a further threshold, the electronic device 100 performs boosting on the output voltage of the battery 142 to avoid abnormal shutdown due to low temperature.

The touch sensor 180K is also called a "touch device". 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 used to detect a touch operation applied thereto or nearby. The touch sensor can communicate the detected touch operation to the application processor to determine the touch event type. Visual output associated with the touch operation may be provided through the display screen 194. In other embodiments, the touch sensor 180K may be disposed on a surface of the electronic device 100, different from the position of the display screen 194.

The bone conduction sensor 180M may acquire a vibration signal. In some embodiments, the bone conduction sensor 180M may acquire a vibration signal of the human vocal part vibrating the bone mass. The bone conduction sensor 180M may also contact the human pulse to receive the blood pressure pulsation signal. In some embodiments, the bone conduction sensor 180M may also be disposed in a headset, integrated into a bone conduction headset. The audio module 170 may analyze a voice signal based on the vibration signal of the bone mass vibrated by the sound part acquired by the bone conduction sensor 180M, so as to implement a voice function. The application processor can analyze heart rate information based on the blood pressure beating signal acquired by the bone conduction sensor 180M, so as to realize the heart rate detection function.

The keys 190 include a power-on key, a volume key, and the like. The keys 190 may be mechanical keys. Or may be touch keys. The electronic apparatus 100 may receive a key input, and generate a key signal input related to user setting and function control of the electronic apparatus 100.

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

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

The SIM card interface 195 is used to connect a SIM card. The SIM card can be brought into and out of contact with the electronic apparatus 100 by being inserted into the SIM card interface 195 or being pulled out of the SIM card interface 195. 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 a Nano SIM card, a Micro SIM card, a SIM card, etc. The same SIM card interface 195 can be inserted with multiple cards at the same time. 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 implement functions such as communication and data communication. In some embodiments, the electronic device 100 employs esims, namely: an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.

Next, an image enhancement processing method and an image enhancement processing apparatus according to an embodiment of the present application are described with reference to fig. 4 to 12.

Fig. 4 is a flowchart illustrating an image texture enhancing method according to an embodiment of the present application. Fig. 10 shows a block diagram of an image texture enhancing apparatus provided according to an embodiment of the present application.

As shown in fig. 10, an image texture enhancing apparatus 1000 provided according to an embodiment of the present application includes an image acquisition module 400, an enhancing module 500, and a fusion module 600.

As shown in fig. 4, an image texture enhancing method according to an embodiment of the present application includes:

and S100, acquiring a visible light image of a shooting object through a visible light sensor of the terminal equipment, and acquiring a hyperspectral image of the shooting object through a hyperspectral sensor of the terminal equipment, wherein the visible light image and the hyperspectral image are obtained through the same exposure.

That is, when a terminal device (for example, a mobile terminal device such as a mobile phone or an IPAD) is used for photographing, a visible light image and a hyperspectral image are simultaneously acquired through one exposure respectively by a visible light sensor and a hyperspectral sensor in the terminal device.

Referring to fig. 10, that is to say, according to the image texture enhancing method of the embodiment of the application, a visible light image and a hyperspectral image of a photographic object are acquired through a visible light sensor and a hyperspectral sensor of a terminal device by an image acquisition module 400 in an image texture enhancing apparatus 1000, where the visible light image and the hyperspectral image are obtained through the same exposure.

Among them, as shown in fig. 7, the visible light image is an RGB three-channel image, and the hyperspectral image is a multi-channel image having up to several tens of channels. That is, the hyperspectral image provides more texture detail information by its spectral information or the like. For example, for hair and clothes which are also black, the texture details are difficult to distinguish under the condition of visible light, and the hyperspectral image can distinguish the hair and clothes by using different spectral information of the hyperspectral image, so that more texture details can be obtained. In addition, for example, compared with three channels of RGB of visible light, the hyperspectral image has up to several tens of channels, the spatial resolution of the image can be improved by extracting texture features of the multichannel image by using the multichannel image, and texture details more than those of the three channels of visible light can be obtained by using the structural self-similarity of the texture feature images of the channels.

And S200, performing texture enhancement processing on the hyperspectral image to obtain a texture enhanced hyperspectral image.

That is, after the visible light image and the hyperspectral image are obtained, the texture enhancement processing is first performed using the hyperspectral image.

Referring to fig. 10, that is, the enhancement module 500 in the image texture enhancing apparatus 1000 performs enhancement processing on the hyperspectral image.

The specific texture enhancement process is described in the following with 2 different embodiments and figures.

And S300, fusing the texture enhanced hyperspectral image and the visible light image to obtain a texture enhanced image.

That is, after the texture enhanced hyperspectral image is obtained, the texture enhanced hyperspectral image and the visible light image are fused to obtain a final texture enhanced image.

Referring to fig. 10, that is, the texture enhanced hyperspectral image and the visible light image are fused by the fusion module 600 in the image texture enhancing apparatus 1000, so as to obtain a final texture enhanced image. As a specific fusion means, for example, a visible light image and a texture-enhanced hyperspectral image may be input to a convolution layer and fused to obtain a final texture-enhanced image.

The details of the enhancement module 500 and the texture enhancement processing steps are described in detail below with reference to two specific embodiments.

Example 1

Fig. 5 is a schematic flowchart illustrating a process of performing texture enhancement processing on a hyperspectral image in an image texture enhancement method according to an embodiment of the present application, and fig. 11 is a block diagram illustrating an enhancement module used in the image texture enhancement method according to the embodiment of fig. 5.

As shown in fig. 11, the enhancement module 500a includes a texture feature extraction module 501a, a segmentation module 502a, a classification network module 503a, and a texture detail processing module 504 a.

As shown in fig. 5, the texture enhancement processing on the hyperspectral image in the image texture enhancement method provided by this embodiment includes the following steps:

in step S201a, for the hyperspectral image, a texture feature is extracted. Thereby, a texture feature image is obtained.

That is, the texture feature extraction module 501a extracts the texture features of the hyperspectral image, and outputs a texture feature image.

As for the extraction method of the texture feature, various methods such as Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), extraction method based on Tamura texture feature, and the like can be used. The kernel principal component analysis method converts a data input space into a feature space by designing a proper kernel function, and then performs feature transformation, so that the non-correlation of pixel elements can be reserved, the interpretability of an image is improved, and texture information of different materials is highlighted.

In step S202a, the hyperspectral image is divided into a plurality of regions by texture division based on spectral information.

That is, for the hyperspectral image, the partitioning process is performed by the segmentation module 502 a. Different material information can be determined according to the spectrum information of the hyperspectral image, so that the spectrum information can be used for partitioning to separate different areas, wherein the same/similar material exists in each area, and the material is different among the different areas.

On the basis, the texture enhancement processing can be carried out on the area needing texture enhancement. Taking portrait photography as an example, the area requiring texture enhancement may be thought of as one or more of a hair area, a skin area, a metal area, a resin/glass area, and a fiber area, for example. Of course, the above description is given by way of example only and is not intended to limit the scope of the present invention. When texture enhancement processing is performed on an imaged image in a scene for taking a portrait photo by a mobile phone, the texture enhancement processing may be performed on a designated area instead of a full image, for example, a hair area (corresponding to an area such as hair and eyebrow), a skin area (corresponding to an area such as a human face), a metal area (corresponding to glasses, metal ornaments, and the like, for example), a resin/glass area (corresponding to an eyeglass lens area), a fiber area (corresponding to an area such as clothes, a hat, and a scarf), that is, texture enhancement may be performed on a human face/body part, and enhancement processing may not be performed on the other background area. When texture enhancement processing is performed on an imaging image in a scene for still photography by a mobile phone or the like, for example, texture detail enhancement processing may be performed on a specified region, for example, a plant region (for photographing plants, flowers, or the like), meat, fish, ceramics (when photographing food on a table), or the like, and no processing may be performed on the other background region.

Step S203a, calibrating the hyperspectral image of the region to be texture-enhanced based on the spectral information thereof by using the classification network model, and obtaining a mask.

That is, after the partitioning process is performed in step S202a, the hyperspectral images of the regions in which the texture enhancement is required are scaled by the classification network model based on the spectral information thereof, and a mask is obtained.

That is, the classification network module 503a calibrates the hyperspectral image of the region in which the texture enhancement is required based on the spectral information thereof, so as to obtain the mask.

Specifically, the classification network model can be formed by training according to the following method:

firstly, obtaining a plurality of samples of different materials corresponding to different areas of a shooting object;

then, acquiring spectral information and corresponding calibration information of each sample;

and then, training by adopting a convolutional neural network based on the sample to obtain the classification network model.

Firstly, obtaining a plurality of samples with different skin colors and the like, hair/hair quality samples with different colors, glasses samples with different materials, cloth samples with different materials and the like; calibrating each sample, and acquiring spectral information of the sample; and training the target function by adopting a convolutional neural network based on the sample to obtain a classification network model. Different samples can be adopted to obtain different classification network models corresponding to different shooting objects. It should be noted that the samples are different for different objects and regions that need enhancement.

Step S204a, performing texture detail enhancement processing on the region needing texture enhancement based on the mask and the texture features to obtain the texture enhanced hyperspectral image.

That is to say, after the mask and the texture feature image are obtained, the texture detail enhancement processing is performed on the region needing texture enhancement based on the mask and the texture feature image, and the texture-enhanced hyperspectral image is obtained. In other words, the texture detail processing module 504a performs the texture detail enhancement processing on the region needing texture enhancement based on the mask and the texture feature image.

Specifically, first, for the region requiring texture enhancement processing, the mask is combined with the texture feature by the combining module 5041a, and then the input texture enhancer module 5042a performs fusion, thereby implementing the texture detail enhancement processing.

The entire flow of the specific method by the enhancement processing given above is further described in detail with reference to fig. 6 and 7.

Fig. 6 is another schematic flow chart of an image texture enhancement method according to an embodiment of the present application, and fig. 7 is an image processed in each step of the image texture enhancement method according to the embodiment shown in fig. 6, where (a) is a visible light image, (b) is a hyperspectral image, (c) is a texture feature image, (d) is a texture-enhanced hyperspectral image, and (e) is a final texture-enhanced image.

That is, first, a visible light image (a) (three-channel image) is obtained by a visible light sensor in the mobile phone, and a hyperspectral image (b) (multi-channel image) is obtained by a hyperspectral sensor in the mobile phone.

And then, extracting the texture features of the hyperspectral image (b) to obtain a texture feature image (c).

Meanwhile, for the hyperspectral image (b), material partitioning is carried out by utilizing spectral information, and calibration is carried out through a classification network to obtain a mask.

And combining the mask and the texture feature image, namely multiplying, taking the multiplication result as input, fusing through a texture enhancement network to enhance the texture details in the mask and restore the texture feature image into a texture enhanced hyperspectral image (d).

And finally, fusing the visible light image (a) and the texture enhanced hyperspectral image to obtain a final texture enhanced image (e).

Example 2

Fig. 8 is a schematic flow chart illustrating a process of performing texture enhancement processing on a hyperspectral image in an image texture enhancement method according to another specific embodiment of the present application, and fig. 12 is a block diagram illustrating an enhancement module used in the image texture enhancement method provided in the embodiment of fig. 8 in fig. 12.

Different from embodiment 1, in this embodiment, the hyperspectral image is not divided, but the full image enters a super-resolution network to be processed, so as to obtain a high-resolution image.

Specifically, as shown in fig. 12, the enhancement module 500b includes: a channel image acquisition module 501b, a super-resolution network module 502b, and a reconstruction module 503 b. More specifically, the super-resolution network module 502b may further include a feature extraction module 5021b, an upsampling module 5022b, and a convolutional layer 5023 b.

As shown in fig. 8, in this embodiment, the texture enhancement processing for the hyperspectral image includes:

step S201b, obtaining each channel image based on the hyperspectral image.

That is, for the hyperspectral image, the channel image acquisition module 501b divides the hyperspectral image according to different channels to obtain each channel image.

Step S202b, based on each channel image, the super-resolution network carries out super-resolution processing to obtain the high-resolution image of each channel.

Then, based on the image of each channel, the super-resolution network module 502b performs super-resolution processing to obtain a high-resolution image of each channel.

More specifically, the super-resolution network module 502b may further include a feature extraction module 5021b,

An up-sampling module 5022b, and a convolutional layer 5023 b. As shown in fig. 9, the super-resolution processing (i.e., step S202b) may include:

in step S202b-1, for each channel image, the texture feature is extracted by the feature extraction module 5021b to obtain a texture feature image of each channel.

The feature extraction module 5021b may be any one of a residual block, a dense block, a cluster block and a residual cluster block. Of course, the feature extraction module is not limited to the above, and may be any module capable of performing feature extraction. The forward propagation of the clustered block comprises two stages, the propagation of the first stage is the same as the propagation of the dense block, and the second stage further refines the characteristics, so that the clustered block can propagate information between layers more easily than the dense block.

Step S202b-2, based on the texture feature image of each channel, the spatial resolution is improved by the up-sampling module 5022 b.

The up-sampling module 5022b may be any one of interpolation up-sampling, deconvolution up-sampling, sub-pixel convolution up-sampling, and clique up-sampling. Of course, the up-sampling module may not be limited thereto. The image reconstruction performance can be further improved by adopting a proper up-sampling module. By the up-sampling module 5022b, the spatial resolution of the input texture feature images of each channel can be improved. Specifically, the spatial resolution of the image is improved by utilizing the structural self-similarity of the texture feature images of all channels. In addition to the above, the clustering upsampling method can obtain finer detail parts by using the self-similarity of the structure and adopting a coefficient mutual learning method.

Step S202b-3, reconstructing the texture feature image (the texture feature image with improved resolution) output by the upsampling module 5022b by the convolutional layer 5023b, and restoring the texture feature image into a high-resolution image of each channel.

That is to say, when the high-resolution characteristic images of each channel are restored by the convolution layer to obtain high resolution, the spectral constraint term is constructed by using the pixels with the spectral similarity within the predetermined threshold range, so that the accuracy of the reconstructed image spectrum can be improved.

And step S203b, fusing the high-resolution images of the channels to obtain the texture enhanced hyperspectral image, wherein the texture enhanced hyperspectral image is a high-resolution multichannel hyperspectral image.

Namely, after the high-resolution images of all channels are obtained, fusion is carried out to obtain the high-resolution multi-channel hyperspectral image.

In addition, the present application also provides a computer program product, which when running the computer program, can implement the image texture enhancing method of the above embodiment.

Further, the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed, the image texture enhancing method of the above embodiment can be implemented.

Next, an apparatus 1200 according to an embodiment of the present application is described with reference to fig. 13. FIG. 13 is a block diagram illustrating an apparatus 1200 according to one embodiment of the present application. The device 1200 may include one or more processors 1201 coupled to a controller hub 1203. For at least one embodiment, the controller hub 1203 communicates with the processor 1201 via a multi-drop Bus such as a Front Side Bus (FSB), a point-to-point interface such as a Quick Path Interconnect (QPI), or similar connection 1206. The processor 1201 executes instructions that control general types of data processing operations. In one embodiment, Controller Hub 1203 includes, but is not limited to, a Graphics Memory Controller Hub (GMCH) (not shown) and an Input/Output Hub (IOH) (which may be on separate chips) (not shown), where the GMCH includes a Memory and a Graphics Controller and is coupled to the IOH.

The device 1200 may also include a coprocessor 1202 and a memory 1204 coupled to the controller hub 1203. Alternatively, one or both of the memory and GMCH may be integrated within the processor (as described herein), with the memory 1204 and coprocessor 1202 being directly coupled to the processor 1201 and to the controller hub 1203, with the controller hub 1203 and IOH being in a single chip. The Memory 1204 may be, for example, a Dynamic Random Access Memory (DRAM), a Phase Change Memory (PCM), or a combination of the two. In one embodiment, coprocessor 1202 is a special-Purpose processor, such as, for example, a high-throughput MIC processor (MIC), a network or communication processor, compression engine, graphics processor, General Purpose Graphics Processor (GPGPU), embedded processor, or the like. The optional nature of coprocessor 1202 is represented in FIG. 13 by dashed lines.

Memory 1204, as a computer-readable storage medium, may include one or more tangible, non-transitory computer-readable media for storing data and/or instructions. For example, the memory 1204 may include any suitable non-volatile memory, such as flash memory, and/or any suitable non-volatile storage device, such as one or more Hard-disk drives (Hard-disk drives, HDD (s)), one or more Compact Discs (CD) drives, and/or one or more Digital Versatile Discs (DVD) drives.

In one embodiment, device 1200 may further include a Network Interface Controller (NIC) 1206. Network interface 1206 may include a transceiver to provide a radio interface for device 1200 to communicate with any other suitable device (e.g., front end module, antenna, etc.). In various embodiments, the network interface 1206 may be integrated with other components of the device 1200. The network interface 1206 may implement the functions of the communication unit in the above-described embodiments.

The device 1200 may further include an Input/Output (I/O) device 1205. I/O1205 may include: a user interface designed to enable a user to interact with the device 1200; the design of the peripheral component interface enables peripheral components to also interact with the device 1200; and/or sensors may be configured to determine environmental conditions and/or location information associated with device 1200.

It is noted that fig. 9 is merely exemplary. That is, although fig. 13 shows that the apparatus 1200 includes a plurality of devices, such as the processor 1201, the controller hub 1203, the memory 1204, etc., in practical applications, an apparatus using the methods of the present application may include only a part of the devices of the apparatus 1200, for example, only the processor 1201 and the NIC1206 may be included. The nature of the alternative device in fig. 13 is shown in dashed lines.

According to some embodiments of the present application, the memory 1204 serving as a computer-readable storage medium stores instructions that, when executed on a computer, enable the system 1200 to perform the image texture enhancement method according to the above embodiments, which may specifically refer to the method of the above embodiments and will not be described herein again.

Fig. 14 is a block diagram of a SoC (System on Chip) 1300 according to an embodiment of the present application. In fig. 14, like parts have the same reference numerals. In addition, the dashed box is an optional feature of more advanced socs. In fig. 14, the SoC1300 includes: an interconnect unit 1350 coupled to the application processor 1310; a system agent unit 1380; a bus controller unit 1390; an integrated memory controller unit 1340; a set or one or more coprocessors 1320 which may include integrated graphics logic, an image processor, an audio processor, and a video processor; a Static Random Access Memory (SRAM) unit 1330; a Direct Memory Access (DMA) unit 1360. In one embodiment, the coprocessor 1320 includes a special-purpose processor, such as, for example, a network or communication processor, compression engine, GPGPU, a high-throughput MIC processor, embedded processor, or the like.

Included in Static Random Access Memory (SRAM) unit 1330 may be one or more computer-readable media for storing data and/or instructions. A computer-readable storage medium may have stored therein instructions, in particular, temporary and permanent copies of the instructions. The instructions may include: when executed by at least one unit in the processor, the Soc1300 may execute the calculation method according to the foregoing embodiment, which may specifically refer to the method of the foregoing embodiment and will not be described herein again.

Embodiments of the mechanisms disclosed herein may be implemented in hardware, software, firmware, or a combination of these implementations. Embodiments of the application may be implemented as computer programs or program code executing on programmable systems comprising at least one processor, a storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device.

Program code may be applied to input instructions to perform the functions described herein and generate output information. The output information may be applied to one or more output devices in a known manner. For purposes of this Application, a processing system includes any system having a Processor such as, for example, a Digital Signal Processor (DSP), a microcontroller, an Application Specific Integrated Circuit (ASIC), or a microprocessor.

The program code may be implemented in a high level procedural or object oriented programming language to communicate with a processing system. The program code can also be implemented in assembly or machine language, if desired. Indeed, the mechanisms described in this application are not limited in scope to any particular programming language. In any case, the language may be a compiled or interpreted language.

In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or via other computer readable media. Thus, a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, floppy diskettes, optical disks, Compact disk Read Only memories (CD-ROMs), magneto-optical disks, Read Only Memories (ROMs), Random Access Memories (RAMs), Erasable Programmable Read Only Memories (EPROMs), Electrically Erasable Programmable Read Only Memories (EEPROMs), magnetic or optical cards, flash Memory, or a tangible machine-readable Memory for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in electrical, optical, acoustical or other forms of propagated signals. Thus, a machine-readable medium includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

In the drawings, some features of the structures or methods may be shown in a particular arrangement and/or order. However, it is to be understood that such specific arrangement and/or ordering may not be required. Rather, in some embodiments, the features may be arranged in a manner and/or order different from that shown in the figures. In addition, the inclusion of a structural or methodical feature in a particular figure is not meant to imply that such feature is required in all embodiments, and in some embodiments, may not be included or may be combined with other features.

It should be noted that, in the embodiments of the apparatuses in the present application, each unit/module is a logical unit/module, and physically, one logical unit/module may be one physical unit/module, or may be a part of one physical unit/module, and may also be implemented by a combination of multiple physical units/modules, where the physical implementation manner of the logical unit/module itself is not the most important, and the combination of the functions implemented by the logical unit/module is the key to solve the technical problem provided by the present application. Furthermore, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units/modules which are not so closely related to solve the technical problems presented in the present application, which does not indicate that no other units/modules exist in the above-mentioned device embodiments.

It is noted that, in the examples and descriptions of this patent, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element.

While the present application has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.

Claims

1. An image texture enhancement method based on hyperspectrum is used for terminal equipment and is characterized by comprising the following steps:

acquiring a visible light image of a shot object through a visible light sensor of the terminal device, and acquiring a hyperspectral image of the shot object through a hyperspectral sensor of the terminal device, wherein the visible light image and the hyperspectral image are obtained through the same exposure;

performing texture enhancement processing on the hyperspectral image to obtain a texture enhanced hyperspectral image;

and fusing the texture enhanced hyperspectral image and the visible light image to obtain a texture enhanced image.

2. The image texture enhancement method according to claim 1, wherein the performing texture enhancement processing on the hyperspectral image comprises:

extracting texture features from the hyperspectral image;

for the hyperspectral image, performing material segmentation according to spectral information to divide the hyperspectral image into a plurality of regions;

calibrating the hyperspectral image of the area needing texture enhancement based on the spectral information of the hyperspectral image through a classification network model to obtain a mask;

and performing texture detail enhancement processing on the region needing texture enhancement based on the mask and the texture features to obtain the texture enhanced hyperspectral image.

3. The image texture enhancement method according to claim 2, characterized in that, for the hyperspectral image, the texture features are extracted by a kernel principal component analysis method.

4. The image texture enhancing method according to claim 2, wherein the region needing texture enhancement comprises one or more of a hair region, a skin region, a metal region, a resin/glass region, and a fiber region.

5. The image texture enhancement method according to claim 2, wherein the classification network model is formed by:

and training by adopting a convolutional neural network based on the sample to obtain the classification network model.

6. The image texture enhancement method according to claim 2, wherein performing the texture detail enhancement processing on the region needing texture enhancement based on the mask and the texture feature comprises: and for the area needing texture enhancement processing, combining the mask and the texture features, and inputting the area into a texture enhancement network for fusion so as to realize the texture detail enhancement processing.

7. The image texture enhancement method according to claim 1, wherein the enhancement processing on the hyperspectral image comprises:

obtaining images of all channels based on the hyperspectral images;

performing super-resolution processing on the basis of the channel images through a super-resolution network to obtain high-resolution images of the channels;

and fusing the high-resolution images of the channels to obtain the texture enhanced hyperspectral image, wherein the texture enhanced hyperspectral image is a high-resolution multichannel hyperspectral image.

8. The image texture enhancement method according to claim 7, wherein the super-resolution network includes a feature extraction module, an upsampling module, and a convolutional layer, and the performing the super-resolution processing based on each channel image through the super-resolution network includes:

extracting texture features of the images of each channel through a feature extraction module to obtain texture feature images of each channel;

based on the texture feature images of each channel, improving the spatial resolution through an up-sampling module;

and reconstructing the texture characteristic image output by the up-sampling module through the convolution layer.

9. The image texture enhancement method according to claim 8, wherein the feature extraction module comprises any one of a residual block, a dense block, a cluster block, and a residual cluster block.

10. The image texture enhancement method of claim 8, wherein the upsampling module comprises any one of interpolated upsampling, deconvolution upsampling, sub-pixel convolution upsampling, and clique upsampling.

11. The image texture enhancement method according to claim 8, wherein in the convolutional layer, spectral constraints are constructed using pixels whose spectral similarity is within a predetermined threshold range.

12. An image texture enhancement device for a terminal device, comprising:

the image acquisition module is used for acquiring a visible light image and a hyperspectral image of a shot object, wherein the visible light image and the hyperspectral image are obtained through the same exposure;

the enhancement module is used for enhancing the hyperspectral image to obtain an enhanced hyperspectral image;

and the fusion module is used for fusing the enhanced hyperspectral image with the visible light image to obtain a texture enhanced image.

13. An electronic device, comprising:

one or more processors;

one or more memories having computer readable code stored therein, which when executed by the one or more processors, causes the processors to perform the image texture enhancement method of any one of claims 1 to 11.

14. A computer readable storage medium having computer readable code stored therein, which when executed by one or more processors, causes the processors to perform the image texture enhancement method of any one of claims 1 to 11.