KR20240078250A - Method for providing image and electronic device for supporting the same - Google Patents

Abstract

An electronic device according to an embodiment includes a display module, a plurality of cameras including a first camera and a second camera having a narrower field of view than the first camera, and positions of the first camera and the second camera. It may include a memory that stores calibration information including the difference between the optical axes of the first camera and the second camera and/or the difference between the optical axes of the first camera and the second camera, and at least one processor. The at least one processor may acquire a first image through the first camera and a second image through the second camera. The at least one processor may determine a first area corresponding to a second image within the first image based on the calibration information. The at least one processor may align the first image and the second image by performing matching on the first area and the second image. The at least one processor may stitch the aligned first image and the second image. The at least one processor may obtain an image to be displayed through the display module by performing an image enhancement operation using the stitched first image and the second image. The at least one processor may update the calibration information based on information for aligning the first image and the second image.

Description

METHOD FOR PROVIDING IMAGE AND ELECTRONIC DEVICE FOR SUPPORTING THE SAME}

Various embodiments of the present disclosure relate to a method of providing an image and an electronic device supporting the same.

An electronic device (eg, a smart phone) may include a plurality of cameras to provide improved camera functions. For example, the electronic device may include an ultra-wide-angle camera, a wide-angle camera, and a telephoto camera, each having different basic zoom magnifications.

An electronic device can provide images with higher quality by combining a plurality of images acquired through each of a plurality of cameras. The electronic device may use calibration (also referred to as “multi calibration”) information of a plurality of cameras to synthesize the plurality of images. Calibration information of the plurality of cameras may include physical position differences between the plurality of cameras (and differences between the optical axes of the plurality of cameras). Calibration information for a plurality of cameras may be obtained using specialized equipment during the process of manufacturing a camera module including a plurality of cameras or during the process of replacing the camera module with another camera module.

Calibration information of a plurality of cameras included in an electronic device may change while using the electronic device. For example, when the electronic device collides with an external object (e.g., when the electronic device falls to the floor and collides with the floor) or when the electronic device deteriorates (e.g., the adhesive force by which a plurality of cameras are attached to the electronic device When this is weakened), physical position differences between the plurality of cameras are changed, and differences between the optical axes of the plurality of cameras may occur (or be changed).

One embodiment of the present disclosure is to obtain a high-pixel image by combining a plurality of images acquired through a plurality of cameras, and to update calibration information based on information acquired while acquiring the high-pixel image. It relates to a method of providing and an electronic device that supports the same.

The technical problems to be achieved by the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by those skilled in the art related to this document from the description below. There will be.

An electronic device according to an embodiment includes a display module, a plurality of cameras including a first camera and a second camera having a narrower field of view than the first camera, and positions of the first camera and the second camera. It may include a memory that stores calibration information including the difference between the optical axes of the first camera and the second camera and/or the difference between the optical axes of the first camera and the second camera, and at least one processor. The at least one processor may acquire a first image through the first camera and a second image through the second camera. The at least one processor may determine a first area corresponding to a second image within the first image based on the calibration information. The at least one processor may align the first image and the second image by performing matching on the first area and the second image. The at least one processor may stitch the aligned first image and the second image. The at least one processor may obtain an image to be displayed through the display module by performing an image enhancement operation using the stitched first image and the second image. The at least one processor may update the calibration information based on information for aligning the first image and the second image.

In one embodiment, a method of providing an image in an electronic device includes acquiring a first image through a first camera and acquiring a second image through a second camera having a narrower field of view than the first camera. may include. The method, based on calibration information including a difference between the positions of the first camera and the second camera and/or a difference between the optical axes of the first camera and the second camera, within the first image An operation of determining a first area corresponding to the second image may be included. The method may include aligning the first image and the second image by performing matching on the first area and the second image. The method may include stitching the aligned first image and the second image. The method may include obtaining an image to be displayed through a display module of the electronic device by performing an image enhancement operation using the stitched first image and the second image. The method may include updating the calibration information based on information for aligning the first image and the second image.

In one embodiment, a non-transitory computer-readable medium having computer-executable instructions recorded thereon, wherein the computer-executable instructions, when executed, cause an electronic device including at least one processor to display a first image through a first camera. can be acquired, and a second image can be acquired through a second camera having a narrower angle of view than that of the first camera. The computer-executable instructions, when executed, cause an electronic device including at least one processor to determine a difference between the positions of the first camera and the second camera and/or the optical axes of the first camera and the second camera. Based on calibration information including the difference between the two images, a first area corresponding to the second image within the first image may be determined. The computer-executable instructions, when executed, cause an electronic device including at least one processor to align the first image and the second image by performing matching on the first area and the second image. You can. When executed, the computer-executable instructions may cause an electronic device including at least one processor to stitch the aligned first image and the second image. When executed, the computer-executable instructions cause an electronic device including at least one processor to perform an image enhancement operation using the stitched first image and the second image, thereby displaying the computer through a display module of the electronic device. You can obtain an image that will be When executed, the computer-executable instructions may cause an electronic device including at least one processor to update the calibration information based on information for aligning the first image and the second image.

A method for providing an image according to an embodiment and an electronic device supporting the same include obtaining a high-pixel image by combining a plurality of images acquired through a plurality of cameras, and based on information acquired while acquiring the high-pixel image. You can update the calibration information by doing this.

1 is a block diagram of an electronic device in a network environment, according to one embodiment.
2 is a block diagram illustrating a camera module, according to one embodiment.
Figure 3 is a block diagram of an electronic device, according to one embodiment.
Figure 4 is a diagram for explaining calibration information according to one embodiment.
Figure 5 is a block diagram of a processor, according to one embodiment.
Figure 6 is a flowchart for explaining a method of providing an image, according to one embodiment.
Figure 7 is a diagram for explaining a method of providing an image, according to an embodiment.
FIG. 8 is a flowchart illustrating a method of correcting image properties according to an embodiment.
FIG. 9 is a diagram for explaining a method of correcting image properties according to an embodiment.
FIG. 10 is a diagram for explaining a method of providing an image, according to an embodiment.
FIG. 11 is a diagram for explaining a method of providing an image according to an embodiment.

1 is a block diagram of an electronic device 101 in a network environment 100, according to one embodiment.

Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197. In some embodiments, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing device) may include a hardware structure specialized for processing artificial intelligence models. Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 can capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 may be a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 can be confirmed or authenticated.

The wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and access to multiple terminals (massive machine type communications (mMTC)), or ultra-reliable and low-latency (URLLC). -latency communications)) can be supported. The wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates. The wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199). According to one embodiment, the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC. Example: Downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected. Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to one embodiment, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199. Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108. For example, when the electronic device 101 must perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

An electronic device according to an embodiment disclosed in this document may be of various types. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments of this document are not limited to the above-described devices.

An embodiment of this document and the terms used herein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various changes, equivalents, or substitutes for the embodiment. In connection with the description of the drawings, similar reference numbers may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the above items, unless the relevant context clearly indicates otherwise. As used herein, “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, and “A Each of phrases such as “at least one of , B, or C” may include any one of the items listed together in the corresponding phrase, or any possible combination thereof. Terms such as "first", "second", or "first" or "second" may be used simply to distinguish one component from another, and to refer to those components in other respects (e.g., importance or order) is not limited. One (e.g., first) component is said to be “coupled” or “connected” to another (e.g., second) component, with or without the terms “functionally” or “communicatively.” When mentioned, it means that any of the components can be connected to the other components directly (e.g. wired), wirelessly, or through a third component.

The term “module” used in one embodiment of this document may include a unit implemented in hardware, software, or firmware, and may be interchangeable with terms such as logic, logic block, component, or circuit, for example. can be used A module may be an integrated part or a minimum unit of the parts or a part thereof that performs one or more functions. For example, according to one embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).

One embodiment of the present document is one or more instructions stored in a storage medium (e.g., built-in memory 136 or external memory 138) that can be read by a machine (e.g., electronic device 101). It may be implemented as software (e.g., program 140) including these. For example, a processor (e.g., processor 120) of a device (e.g., electronic device 101) may call at least one command among one or more commands stored from a storage medium and execute it. This allows the device to be operated to perform at least one function according to the at least one instruction called. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. A storage medium that can be read by a device may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' only means that the storage medium is a tangible device and does not contain signals (e.g. electromagnetic waves). This term refers to cases where data is stored semi-permanently in the storage medium. There is no distinction between temporary storage cases.

According to one embodiment, a method according to an embodiment disclosed in this document may be provided and included in a computer program product. Computer program products are commodities and can be traded between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g. compact disc read only memory (CD-ROM)), or through an application store (e.g. Play Store ^TM ) or on two user devices (e.g. It can be distributed (e.g. downloaded or uploaded) directly between smart phones) or online. In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

According to one embodiment, each component (e.g., module or program) of the above-described components may include a single or multiple entities, and some of the multiple entities may be separately placed in other components. . According to one embodiment, one or more of the above-described corresponding components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (eg, modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each component of the plurality of components identically or similarly to those performed by the corresponding component of the plurality of components prior to the integration. . According to one embodiment, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or , or one or more other operations may be added.

Figure 2 is a block diagram 200 illustrating a camera module 180, according to one embodiment.

Referring to FIG. 2, the camera module 180 includes a lens assembly 210, a flash 220, an image sensor 230, an image stabilizer 240, a memory 250 (e.g., buffer memory), or an image signal processor. It may include (260). The lens assembly 210 may collect light emitted from a subject that is the target of image capture. Lens assembly 210 may include one or more lenses. According to one embodiment, the camera module 180 may include a plurality of lens assemblies 210. In this case, the camera module 180 may form, for example, a dual camera, a 360-degree camera, or a spherical camera. Some of the plurality of lens assemblies 210 have the same lens properties (e.g., angle of view, focal length, autofocus, f number, or optical zoom), or at least one lens assembly is different from another lens assembly. It may have one or more lens properties that are different from the lens properties of . The lens assembly 210 may include, for example, a wide-angle lens or a telephoto lens.

The flash 220 may emit light used to enhance light emitted or reflected from a subject. According to one embodiment, the flash 220 may include one or more light emitting diodes (eg, red-green-blue (RGB) LED, white LED, infrared LED, or ultraviolet LED), or a xenon lamp. The image sensor 230 may acquire an image corresponding to the subject by converting light emitted or reflected from the subject and transmitted through the lens assembly 210 into an electrical signal. According to one embodiment, the image sensor 230 is one image sensor selected from among image sensors with different properties, such as an RGB sensor, a BW (black and white) sensor, an IR sensor, or a UV sensor, and the same It may include a plurality of image sensors having different properties, or a plurality of image sensors having different properties. Each image sensor included in the image sensor 230 may be implemented using, for example, a charged coupled device (CCD) sensor or a complementary metal oxide semiconductor (CMOS) sensor.

The image stabilizer 240 moves at least one lens or image sensor 230 included in the lens assembly 210 in a specific direction in response to the movement of the camera module 180 or the electronic device 101 including the same. The operating characteristics of the image sensor 230 can be controlled (e.g., adjusting read-out timing, etc.). This allows to compensate for at least some of the negative effects of said movement on the image being captured. According to one embodiment, the image stabilizer 240 uses a gyro sensor (not shown) or an acceleration sensor (not shown) disposed inside or outside the camera module 180 to stabilize the camera module 180 or the electronic device 101. ) can detect such movements. According to one embodiment, the image stabilizer 240 may be implemented as, for example, an optical image stabilizer. The memory 250 may at least temporarily store at least a portion of the image acquired through the image sensor 230 for the next image processing task. For example, when image acquisition is delayed due to the shutter or when multiple images are acquired at high speed, the acquired original image (e.g., Bayer-patterned image or high-resolution image) is stored in the memory 250. , the corresponding copy image (e.g., low resolution image) may be previewed through the display module 160. Thereafter, when a specified condition is satisfied (eg, user input or system command), at least a portion of the original image stored in the memory 250 may be obtained and processed, for example, by the image signal processor 260. According to one embodiment, the memory 250 may be configured as at least part of the memory 130 or as a separate memory that operates independently.

The image signal processor 260 may perform one or more image processes on an image acquired through the image sensor 230 or an image stored in the memory 250. The one or more image processes may include, for example, depth map creation, three-dimensional modeling, panorama creation, feature point extraction, image compositing, or image compensation (e.g., noise reduction, resolution adjustment, brightness adjustment, blurring). Additionally or alternatively, the image signal processor 260 may include blurring, sharpening, or softening, and may include at least one of the components included in the camera module 180 (eg, an image sensor). The image processed by the image signal processor 260 may be stored back in the memory 250 for further processing. Alternatively, it may be provided as an external component of the camera module 180 (e.g., memory 130, display module 160, electronic device 102, electronic device 104, or server 108). According to an example, the image signal processor 260 may be configured as at least a part of the processor 120, or the image signal processor 260 may be configured as a separate processor that operates independently of the processor 120. When configured as a separate processor, at least one image processed by the image signal processor 260 may be displayed through the display module 160 as is or after additional image processing by the processor 120.

According to one embodiment, the electronic device 101 may include a plurality of camera modules 180, each having different properties or functions. In this case, for example, at least one of the plurality of camera modules 180 may be a wide-angle camera, and at least another one may be a telephoto camera. Similarly, at least one of the plurality of camera modules 180 may be a front camera, and at least another one may be a rear camera.

Figure 3 is a block diagram of an electronic device 301, according to one embodiment.

Referring to FIG. 3 , in one embodiment, the electronic device 301 may include a display module 310, a camera module 320, a memory 330, and/or a processor 340.

In one embodiment, display module 310 may be display module 160 of FIG. 1 .

In one embodiment, the display module 310 may display an image acquired by the camera module 320. For example, the display module 310 may display dynamic images (e.g., preview images, video images) and/or still images (e.g., capture images) acquired by the camera module 320. It can be displayed.

In one embodiment, camera module 320 may be camera module 180 of FIG. 1 or FIG. 2 .

In one embodiment, the camera module 320 may include a plurality of cameras (hereinafter referred to as “plurality of cameras”) having different fields of view. For example, the camera module 320 includes a first camera 321 (also referred to as an “ultra wide camera”) having an angle of view ranging from about 100 degrees to about 120 degrees, and a second camera having an angle of view of about 80 degrees. It may include a second camera 322 (also referred to as a “wide camera”), and a third camera 323 (also referred to as a “telephoto camera”) having an angle of view ranging from about 10 degrees to about 40 degrees. there is. In one embodiment, the focal lengths of the lens of the first camera 321, the lens of the second camera 322, and the lens of the third camera 323 may be different. For example, based on a full frame sensor (or 35mm film camera), the focal length of the lens of the first camera 321 is about 7mm to about 15mm, and the focal length of the lens of the second camera 322 is It is about 15mm to about 35mm, and the focal length of the lens of the third camera 323 may be about 70mm to about 250mm. However, the angles of view and focal lengths of the plurality of cameras described above may be examples.

In one embodiment, the camera module 320 in FIG. 3 is illustrated as including a first camera 321, a second camera 322, and a third camera 323, but is not limited thereto. For example, the camera module 320 does not include the third camera 323, or includes the first camera 321 in addition to the first camera 321, the second camera 322, and the third camera 323. , may include an additional camera having a view angle different from that of the second camera 322 and the third camera 323.

In one embodiment, memory 330 may be memory 130 of FIG. 1 or memory 250 of FIG. 2 .

In one embodiment, the memory 330 may store information for performing an operation to provide an image.

In one embodiment, memory 330 may further include calibration information 331.

In one embodiment, the calibration information 331 includes physical position differences between the plurality of cameras 321, 322, and 323 (and differences between the optical axes of the plurality of cameras 321, 322, and 323). may include. Hereinafter, with reference to FIG. 4, the calibration information 331 will be described in more detail.

FIG. 4 is a diagram for explaining calibration information 331 according to an embodiment.

Referring to FIG. 4, in one embodiment, reference numeral 401 denotes a plurality of cameras 321, 322, and 323, respectively, after calibration (e.g., calibration performed during the process of the camera module 320) is performed. The angle of view areas 410, 420, and 430 may be indicated. For example, the angle of view area 410 may represent a scene of a subject captured by the first camera 321. The angle of view area 420 may represent a scene of a subject captured by the second camera 322. The angle of view area 430 may represent a scene of a subject captured by the third camera 323. The angle of view area 410 may include the angle of view area 420 and the angle of view area 430 , and the angle of view area 420 may include the angle of view area 430 .

In one embodiment, as shown by reference numeral 401, the difference between the positions where the plurality of cameras 321, 322, and 323 are disposed in the electronic device 301 (e.g., the plurality of cameras 321, 322, 323) There may be distances between each other. As the positions where the plurality of cameras 321, 322, and 323 are placed in the electronic device 301 are different from each other, the optical axes of the plurality of cameras 321, 322, and 323 (e.g., the plurality of cameras 321, The axis passing through the center of each angle of view (322, 323) may be different. The optical axis of the camera may refer to the axis passing through the center of the camera lens and the center of the image sensor (or the axis passing through the center of the camera's angle of view). When calibration is performed on the plurality of cameras 321, 322, and 323, the optical axes of the plurality of cameras 321, 322, and 323 are parallel to each other. , the arrangement can be adjusted (e.g., the optical axis can be adjusted). After calibration is performed, the difference between the positions where the plurality of cameras 321, 322, and 323 are placed on the electronic device 301 (or the positions where the plurality of cameras 321, 322, and 323 are placed on the electronic device 301) Calibration information 331 including (positioned positions) may be stored in the memory 330 of the electronic device 301.

In one embodiment, when the electronic device 301 including a plurality of cameras 321, 322, and 323 collides with an external object (e.g., when the electronic device 301 falls to the floor and collides with the floor surface) ) or when the electronic device 301 is aged (e.g., when the adhesive force attaching the plurality of cameras 321, 322, and 323 to the electronic device 301 is weakened), the plurality of cameras 321, 322, 323) locations may be changed. For example, at reference numeral 401, the position of one of the positions of the plurality of cameras 321, 322, and 323 may be changed on the X-axis, Y-axis, and/or Z-axis. In this case, when the positions of the plurality of cameras 321, 322, and 323 change, axes passing through the centers of the angles of view of the plurality of cameras 321, 322, and 323 may change.

In one embodiment, when the electronic device 301 including the plurality of cameras 321, 322, and 323 collides with an external object or when the electronic device 301 becomes old, the plurality of cameras 321, 322 , 323) differences may occur between the optical axes. For example, by performing calibration on the plurality of cameras 321, 322, and 323, the optical axes of the plurality of cameras 321, 322, and 323, which were parallel to each other, may become non-parallel.

In one embodiment, one of the optical axes of the plurality of cameras 321, 322, and 323 may be changed (eg, rotated) based on the X-axis, Y-axis, and/or Z-axis. For example, as shown at reference numeral 402, the optical axis of the second camera 322 may be rotated based on the Z-axis. When the optical axis of the second camera 322 is rotated about the Z axis, the angle of view area (e.g., the angle of view area 420) and the optical axis of the second camera 322 are aligned before the optical axis of the second camera 322 is rotated. After rotation, the angle of view area (eg, angle of view area 421) may be different. Hereinafter, a plurality of cameras (321, 322, 323) generated by changing one of the optical axes of the plurality of cameras (321, 322, 323) based on the X-axis, Y-axis, and/or Z-axis. Differences between the optical axes (e.g., angles formed between the optical axes of the plurality of cameras 321, 322, 323) are referred to as “differences between the optical axes of the plurality of cameras” or “angles of view of the plurality of cameras ( It will be referred to as “distortion between optical axes”.

In one embodiment, the electronic device 301 (e.g., processor 340) synthesizes a plurality of images acquired through each of the plurality of cameras 321, 322, and 323 based on the calibration information 331. can do.

In one embodiment, the electronic device 301 (e.g., processor 340) collects information (e.g., a plurality of information acquired while aligning the images), the difference between the positions of the plurality of cameras 321, 322, and 323 and/or the optical axes of the plurality of cameras 321, 322, and 323 Calibration information 331 including the difference between the two may be updated (and stored in the memory 330). The operation of the electronic device 301 to synthesize a plurality of images acquired through each of the plurality of cameras 321, 322, and 323 and the operation of updating the calibration information 331 will be described in detail later.

Referring to Figure 3, in one embodiment, processor 340 may be processor 120 of Figure 1.

In one embodiment, the processor 340 may control overall operations for providing images. In one embodiment, processor 340 may include one or more processors to provide images.

In one embodiment, the processor 340 may include a plurality of components to perform an operation of providing an image. A plurality of components included in the processor 340 will be described in detail with reference to FIG. 5.

In FIG. 3 , the electronic device 301 is illustrated as including a display module 310, a camera module 320, a memory 330, and/or a processor 340, but is not limited thereto. For example, the electronic device 301 may further include at least one component shown in FIG. 1 .

Figure 5 is a block diagram of the processor 340, according to one embodiment.

Referring to Figure 5, in one embodiment, the processor 340 includes a correction module 510, an alignment module 520, a stitching module 530, an image enhancement module 540, and/or a calibration information management module ( 550).

In one embodiment, the correction module 510 is based on the calibration information 331 obtained from the calibration information management module 550, a plurality of images acquired from each of the plurality of cameras 321, 322, and 323. Correction can be performed. For example, when the first image and the second image are acquired through each of the first camera 321 and the second camera 322, the correction module 510 uses the first camera 321 and the second camera ( Based on the calibration information related to 322), the first area corresponding to the second image within the first image may be determined (eg, detected). The correction module 510 provides information for changing the properties of the first image so that the properties of the first image are the same as the properties of the second image, based on the pixel values of the first area and the pixel values of the second image. can be obtained. The correction module 510 may correct the first image and/or the second image based on information for changing the properties of the first image.

In one embodiment, the correction module 510 may further perform an operation to correct the brightness of the first image and/or the brightness of the second image.

In one embodiment, the calibration module 510 configures parameters related to settings of the first camera 321 and/or the second camera 322 such that the first image and the second image have the same exposure and/or white balance. (parameters) can be controlled (e.g., set, adjusted).

A more detailed description of the operation performed by the correction module 510 will be described in detail later with reference to FIGS. 8 and 9.

In one embodiment, the alignment module 520 determines a first region within the first image corresponding to the second image, based on calibration information associated with the first camera 321 and the second camera 322 ( Example: detection) can be performed.

In one embodiment, the alignment module 520 may align the first image and the second image by performing matching on the first area and the second image.

In one embodiment, the alignment module 520 may acquire information for aligning the first image and the second image by performing matching on the first area and the second image.

In one embodiment, the stitching module 530 may stitch (eg, combine) the aligned first and second images.

In one embodiment, the image enhancement module 540 may obtain an image to be displayed through the display module 310 by performing an image enhancement operation using the first image and the second image. For example, the image enhancement module 540 may obtain an image to be displayed through the display module 310 by performing a super resolution (SR) operation on the first image and the second image.

In one embodiment, the calibration information management module 550 may manage the calibration information 331 stored in the memory 330. For example, the calibration information management module 550 updates the calibration information 331 stored in the memory 330 based on the information for aligning the first image and the second image obtained in the alignment module 520. can do.

Operations performed by the alignment module 520, stitching module 530, SR module 540, and/or calibration information management module 550 will be described in detail below with reference to FIG. 6.

In Figure 5, the processor 340 is illustrated as including a correction module 510, an alignment module 520, a stitching module 530, an image enhancement module 540, and/or a calibration information management module 550. , but is not limited to this. For example, at least two of the correction module 510, alignment module 520, stitching module 530, image enhancement module 540, and/or calibration information management module 550 are integrated into one module. and can be implemented. For example, the processor 340 includes at least one of the correction module 510, alignment module 520, stitching module 530, image enhancement module 540, and/or calibration information management module 550. may not include. For example, in addition to the correction module 510, alignment module 520, stitching module 530, image enhancement module 540, and/or calibration information management module 550, it may further include at least one module. there is.

The electronic device 301 according to an embodiment includes a display module 320, a first camera 321, and a plurality of devices including a second camera 322 having a narrower field of view than the first camera 321. Calibration comprising differences between the positions of the cameras, the first camera 321 and the second camera 322 and/or the difference between the optical axes of the first camera 321 and the second camera 322 It may include a memory 330 that stores information 331, and at least one processor 340. The at least one processor 340 may acquire a first image through the first camera 321 and a second image through the second camera 322. The at least one processor 340 may determine a first area corresponding to the second image within the first image based on the calibration information 331. The at least one processor 340 may align the first image and the second image by performing matching on the first area and the second image. The at least one processor 340 may stitch the aligned first image and the second image. The at least one processor 340 may obtain an image to be displayed through the display module 320 by performing an image enhancement operation using the stitched first image and the second image. The at least one processor 340 may update the calibration information 331 based on information for aligning the first image and the second image.

In one embodiment, the at least one processor 340 acquires first feature points from the first area, acquires second feature points corresponding to the first feature points with the second image, and It may be configured to obtain information for aligning the first image and the second image based on the positions of the first feature points and the positions of the second feature points.

In one embodiment, the at least one processor 340 may be configured to align the second image within the first image based on a difference between the positions of the first feature points and the positions of the second feature points. You can.

In one embodiment, the information for aligning the first image and the second image includes information for positioning the second image within the first image and/or information for warping the second image. may include.

In one embodiment, the at least one processor 340 determines the positions of the first camera 321 and the second camera 322 based on information for locating the second image within the first image. It may be configured to update the difference between the optical axes of the first camera 321 and the second camera 322 based on information for warping of the second image.

In one embodiment, the at least one processor 340 obtains information for converting an attribute of the first image based on the first area and the second image, and the obtained Based on the information, the first image may be corrected so that the properties of the first image are the same as the properties of the second image.

In one embodiment, the at least one processor 340 uses a histogram for the first image and the second image to calculate the average of pixel values of the first area and the pixel of the second image. Obtain an average of the values, obtain data for transforming the pixel values of the first region such that the average of the pixel values of the first region and the average of the pixel values of the second image are the same, and the obtained and may be configured to apply data to the first image.

In one embodiment, the at least one processor 340 may be configured to perform upscaling on the first image such that the size of the first area is the same as the size of the second image. .

In one embodiment, the at least one processor 340 may be configured to perform an SR operation on the stitched first image and the second image using a learned artificial intelligence model.

In one embodiment, the at least one processor 340 acquires an image to be displayed through the display module 320 and then selects a point of the acquired image based on a user input that changes the zoom factor. It may be configured to enlarge or reduce the acquired image as a reference.

FIG. 6 is a flowchart 600 for explaining a method of providing an image, according to an embodiment.

Figure 7 is a diagram for explaining a method of providing an image, according to an embodiment.

6 and 7, in operation 601, in one embodiment, the processor 340 captures a first image through the first camera 321 (hereinafter referred to as an image acquired through the first camera 321). It is possible to acquire a “first image”) and acquire a second image through the second camera 322 (hereinafter, the image acquired through the second camera 322 is referred to as the “second image”). there is.

In one embodiment, the processor 340 may acquire the first image and the second image substantially simultaneously through the first camera 321 (ultra-wide-angle camera) and the second camera 322 (wide-angle camera). .

In one embodiment, the processor 340 may acquire a first image through the first camera 321 and then acquire a second image through the second camera 322 within a specified time.

In one embodiment, the scene represented by the first image may include the scene represented by the second image. For example, the scene represented by the first image may further include a scene existing within the field of view area of the first camera 321 in addition to the scene represented by the second image.

In one embodiment, the first image and the second image may be preview images acquired in real time. However, the present invention is not limited thereto, and the first image and the second image may be captured images obtained based on user input.

In one embodiment, the first image and the second image are acquired by the first camera 321 and the second camera 322, respectively, and then stored in the memory 330 (e.g., a gallery application). These can be images.

Although FIG. 6 illustrates that a first image is acquired through the first camera 321 and a second image is acquired through the second camera 322, the present invention is not limited thereto. For example, the processor 340 acquires a first image through the first camera 321, a second image through the second camera 322, and a third image through the third camera 323. Images can be acquired substantially simultaneously (or sequentially within a specified time). Below, for convenience of explanation, an example in which the processor 340 acquires a first image through the first camera 321 and a second image through the second camera 322 will be described.

In one embodiment, the processor 340 is configured to: correct at least one attribute of the first image and/or the second image after the first image and the second image are acquired (and before performing operation 603). can be performed. The operation of correcting at least one attribute of the first image and the second image will be described later with reference to FIGS. 8 and 9.

At operation 603, in one embodiment, the processor 340, based on the calibration information 331, selects a first region within the first image corresponding to the second image (hereinafter, within the first image to the second image). The corresponding area is referred to as “the first area”) can be determined.

In one embodiment, the calibration information 331 may include differences between positions of a plurality of cameras and/or differences between optical axes of a plurality of cameras. In one embodiment, the calibration information 331 is a difference between the positions of a plurality of cameras (e.g., a difference between the positions of the first camera 321 and the second camera 322, the first camera 321 and the second camera 322). Differences between the positions of the three cameras 323, and/or differences between the positions of the second camera 322 and the third camera 323) and/or differences between the optical axes of a plurality of cameras (e.g., the first camera Difference (e.g., angle) between the optical axis of 321 and the optical axis of the second camera 322, the difference between the optical axis of the first camera 321 and the optical axis of the third camera 323, and/or the optical axis of the second camera 322 ) may be information for minimizing the disparity between the first image and the second image that occurs due to the difference between the optical axis of the camera 323 and the optical axis of the third camera 323.

In one embodiment, the first area corresponding to the second image within the first image may be an area containing substantially the same scene as the scene represented by the second image. In one embodiment, the first area corresponding to the second image within the first image may be an area containing feature points that are substantially the same as feature points included in the second image. In one embodiment, the first region corresponding to the second image within the first image may be an region including a region of interest that is substantially the same as a region of interest included in the second image.

In one embodiment, the processor 340 determines a second image within the first image based on the difference between the positions of the plurality of cameras and/or the difference between the optical axes of the plurality of cameras included in the calibration information 331. The first area corresponding to the image may be determined (eg, detected).

Although not shown in FIG. 6, in one embodiment, the processor 340 determines the lens distortion occurring within the first image and the second image before determining the first region within the first image corresponding to the second image. Compensation is possible. For example, the processor 340 may check the lens distortion value of the lens of the first camera 321 and the lens distortion value of the lens of the second camera 322 stored in the memory 330. The processor 340 calculates lens distortion (e.g., lens distortion) occurring in the first image and the second image based on the lens distortion value of the lens of the first camera 321 and the lens distortion value of the lens of the second camera 322. radiation distortion) can be compensated for.

Although not shown in FIG. 6, in one embodiment, processor 340 may perform upscaling on the first image before determining a first region within the first image corresponding to the second image. You can. In one embodiment, the size of the first image acquired through the first camera 321 and the size of the second image acquired through the second camera 322 may be the same. For example, both the first image and the second image may have a resolution of 12M (e.g., 4000*3000 (4000 pixel values horizontally and 3000 pixel values vertically)). On the other hand, the scene represented by the first image may include the scene represented by the second image. The processor 340 performs upscaling on the first image such that the size of the first area corresponding to the second image within the first image is substantially the same as (e.g., corresponds to) the size of the second image. Example: The first image can be enlarged.

At operation 605, in one embodiment, the processor 340 may align the first image and the second image by performing matching on the first area and the second image.

In one embodiment, when the first area in the first image is replaced with the second image, the first image and the second image may not be aligned. For example, when deleting a first area within a first image and attaching a second image to the location of the first area, the border of the first image and the border of the second image that adjoin each other may not be aligned (e.g. : may not match).

In one embodiment, reference numeral 701 in FIG. 7 refers to a first image 710 acquired by the first camera 321 (e.g., an image acquired by the first camera 321 and then upscaled), a second image 710 acquired by the first camera 321, and then upscaled. A second image 720 acquired by the camera 322 (e.g., an image acquired by the second camera 322 and then upscaled), and a third image 730 acquired by the third camera 323 ) can display overlapping screens. Reference numeral 702 in FIG. 7 may represent an enlarged image of the area 740 within the screen of reference numeral 701. At reference numeral 702, area 711 may be part of the first image 710, area 721 may be part of the second image 720, and area 731 may be part of the third image 730. . Like the area 750 of reference numeral 702 in FIG. 7 , the boundary of the area 711 and the boundary of the area 721 may not coincide with each other. For example, while a single number "5" is printed on a calendar to be photographed, two "5"s may be displayed in area 750, such as area 750 at reference numeral 702 in FIG. 7. there is.

In one embodiment, the processor 340 may perform matching on the first area and the second image. For example, the processor 340 generates feature points from the first area (hereinafter referred to as “first feature points”) and feature points corresponding to the first feature points from the second image (hereinafter referred to as “second feature points”). ") can be obtained (e.g. extracted). The processor 340 may check the positions of first feature points within the first area and confirm the positions of second feature points within the second area. The processor 340 generates a first image and a second image based on the positions of the first feature points and the positions of the second feature points (e.g., the difference between the positions of the first feature points and the positions of the second feature points). Information for sorting can be obtained. The processor 340 generates a first image and a second image based on the positions of the first feature points and the positions of the second feature points (e.g., the difference between the positions of the first feature points and the positions of the second feature points). You can sort.

In one embodiment, the processor 340 may align the first image and the second image based on information for aligning the first image and the second image. For example, the processor 340 may align the second image within the first image based on information for aligning the first image and the second image. For example, the processor 340, based on information for aligning the first image and the second image, determines the scene represented by the area including the boundary with the second image within the first image and the best image of the second image. The act of positioning a second image within a first image so that the scene represented by the spot area continues seamlessly, and/or warping (e.g., rotation transformation) of the second image (and/or the first image) (rotation transform) and/or perspective transform) can be performed.

In one embodiment, the information for aligning the first image and the second image is such that the scene represented by the area including the boundary with the second image within the first image and the scene represented by the edge area of the second image are disconnected. Information for positioning the second image within the first image (e.g., information about where the second image will be stitched within the first image) and/or the second image (and/or the first image) ) may include information for warping (rotation transformation and/or perspective transformation).

In one embodiment, the information for locating the second image within the first image includes the location of the first area corresponding to the second image within the first image, and the location of the first area after the first image and the second image are aligned. It may include differences between the positions of the second image within the first image.

In one embodiment, the information for warping the second image (and/or the first image) includes rotating and/or rotating the second image with respect to the first image so that the first image and the second image are aligned. Alternatively, it may include the angle (or the amount of change in angle) used for perspective conversion.

At operation 607, in one embodiment, processor 340 may stitch the first image and the second image. For example, the processor 340 may stitch (eg, combine) the aligned first and second images.

In one embodiment, as part of the operation of stitching the aligned first and second images, the processor 340 aligns the first and second images and then adds Overlapping and/or blending (e.g., alpha blending) may be performed. For example, the processor 340 divides a portion of the second area (e.g., an area including the boundary of the first image and the second image within the first image) excluding the second image within the first image into the second area. Overlapping may be performed by replacing a part of an image, or replacing a part of a second image (e.g., an area including the boundary between the first image and the second image within the second image) with a part of the second area. For example, the processor 340 may perform blending to adjust the transparency of the first image and the transparency of the second image.

In operation 609, in one embodiment, the processor 340 may obtain an image to be displayed through the display module 310 by performing an image enhancement operation using the first image and the second image. For example, the processor 340 may obtain an image to be displayed through the display module 310 by performing an image enhancement operation on the stitched first and second images through operation 607.

In one embodiment, an image enhancement operation may include an operation that can improve the quality (or picture quality) of an image. For example, an image enhancement operation may include an SR operation. However, it is not limited to this.

In one embodiment, the SR operation may refer to an operation of acquiring an image with high resolution from one image or multiple images with low resolution. For example, SR operation is a single image super resolution (SISR) (also referred to as “single frame super resolution” (SFSR)) method of acquiring one high-resolution image from one low-resolution image or from multiple images. This may be an operation using the MISR (multi image super resolution) method of acquiring a single high-resolution image.

In one embodiment, the processor 340 uses a learned artificial intelligence model to create, within the stitched first and second images, details of the second image that are enhanced and patterns of the second image that are stitched using the learned artificial intelligence model. ), an SR operation may be performed to enhance the details of the first image (e.g., an area within the first image excluding the second image).

In one embodiment, the processor 340 performs an SR operation on the stitched first image and the second image using an internal patch-based artificial intelligence model or a supervised CNN-based artificial intelligence model as a learned artificial intelligence model. can do. However, the learned artificial intelligence model used for SR operation is not limited to the examples described above.

Although not shown in FIG. 6 , in one embodiment, the processor 340 may display an image obtained by performing an image enhancement operation using the first image and the second image through the display module 310.

At operation 611, in one embodiment, processor 340 updates calibration information 331 stored in memory 330 based on information for aligning the first image and the second image. You can (update).

In one embodiment, as described above, the processor 340 may obtain information for aligning the first image and the second image by performing matching on the first area and the second image in operation 605. . In one embodiment, the information for aligning the first image and the second image is such that the scene represented by the area including the border with the second image within the first image and the scene represented by the edge area of the second image are disconnected. may include information for positioning the second image within the first image and/or for warping (e.g., rotation and/or perspective transformation) of the second image (and/or the first image) You can.

In one embodiment, the information for locating the second image within the first image includes the location of the first area corresponding to the second image within the first image, and the location of the first area after the first image and the second image are aligned. It may include differences between the positions of the second image within the first image.

In one embodiment, the processor 340 controls the first camera 321 and the first camera 321 included in the calibration information 331 stored in the memory 330, based on information for locating the second image within the first image. The position difference between the two cameras 322 can be updated. For example, processor 340 may determine the difference between the position of the first region within the first image that corresponds to the second image and the position of the second image within the first image after the first and second images are aligned. Based on , the amount of change in the position difference between the first camera 321 and the second camera 322 can be obtained (eg, calculated). The processor 340 determines the first camera 321 and The position difference between the second cameras 322 can be updated.

In one embodiment, the information for warping the second image (and/or the first image) includes the angle (or the change in angle) used to rotate and/or perspective transform the second image with respect to the first image. ) may include.

In one embodiment, the processor 340 operates the first camera included in the calibration information 331 stored in the memory 330 based on information for warping of the second image (and/or the first image). The difference between the optical axes of the camera 321 and the second camera 322 can be updated. For example, the processor 340 determines the difference between the optical axes of the first camera 321 and the second camera 322 based on information for warping of the second image (and/or the first image). The amount of change can be obtained (e.g. calculated). The processor 340 determines the difference between the optical axes of the first camera 321 and the second camera 322 based on the obtained change in the difference between the optical axes of the first camera 321 and the second camera 322. can be updated.

FIG. 8 is a flowchart 800 illustrating a method of correcting image properties according to an embodiment.

FIG. 9 is a diagram 900 for explaining a method of correcting image properties according to an embodiment.

In one embodiment, FIG. 8 may be a diagram for explaining an operation of correcting the first image and/or the second image after performing operation 601 of FIG. 6 and before performing operation 603.

Referring to FIGS. 8 and 9 , in operation 801, in one embodiment, the processor 340 may determine a first area corresponding to the second image within the first image based on calibration information. For example, as shown in FIG. 9, the processor 340 acquires the first image 910 through the first camera 321 and acquires the second image 920 through the second camera 322. After acquisition, based on the calibration information 331, the first area 930 corresponding to the second image within the first image may be determined.

Since operation 801 is at least partially the same or similar to operation 603 of FIG. 6, overlapping descriptions will be omitted.

In one embodiment, when operation 801 is performed, operation 603 in FIG. 6 may not be performed.

At operation 803, in one embodiment, the processor 340 may obtain information for converting attributes of the first image and/or the second image based on the first area and the second image. .

In one embodiment, the properties of the first image and/or second image include color, saturation, exposure, or white balance (e.g., color temperature) of the first image and/or second image. It may include at least one of: However, the properties of the first image and/or the second image are not limited to the above-described examples.

In one embodiment, the information for converting the properties of the first image and/or the second image is based on the pixel value of the first area and the pixel value of the second image, such that the properties of the first image are those of the second image. It may include information for changing the properties of the first image so that they become the same as the properties.

In one embodiment, processor 340 determines a relationship between pixel values of the first region and pixel values of the second image (e.g., between pixel values of the first region and pixel values of the second image, at each corresponding pixel location). Based on the difference), obtain (e.g., create) data (e.g., a look up table, function, equation, or constant values) for converting the properties of the first region to the properties of the second image. can do. For example, in FIG. 9, the processor 340 generates an attribute (e.g., attribute value) of the first area based on the pixel value of the first area 930 and the pixel value of the second image 920. 2 Data 940 for conversion into image properties (e.g., property values) can be obtained.

At operation 805, in one embodiment, the processor 340 may correct the first image and/or the second image based on information to transform properties of the first image and/or the second image.

In one embodiment, the processor 340 converts the properties of the first image to the properties of the second image, based on data (e.g., data 940) for converting the properties of the first area into the properties of the second image. The properties of the first image may be converted to be the same as . For example, the processor 340 divides data for converting properties of the first area into properties of the second image into a first area and a first area within the first image (e.g., first area 930). ) can be applied to areas other than (e.g. area 931). The processor 340 applies data for converting the properties of the first area to the properties of the second image to the first image, thereby creating a first image (e.g., image 950) having the same properties as the second image. It can be obtained. Within the image 950 of FIG. 9, area 930 is an area obtained by applying data for converting the properties of the first area to the properties of the second image to the first area 930, and area 960 ) may be an area obtained by applying data for converting the properties of the first area to the properties of the second image to the area 931.

In the above-described examples, examples of converting the properties of the first image to the properties of the second image so that the properties of the second image and the properties of the first image are the same have been described, but the present invention is not limited thereto. For example, the processor 340 may convert the properties of the second image into the properties of the first image so that the properties of the second image and the properties of the first image are the same.

In one embodiment, the processor 340 may further perform an operation to correct the brightness of the first image and/or the brightness of the second image. For example, the processor 340 obtains (e.g., calculates) the average of the pixel values of the first area and the average of the pixel values of the second image using the histogram for the first image and the second image. )can do. The processor 340 may obtain (eg, calculate) data for converting the pixel values of the first area so that the average of the pixel values of the first area and the average of the pixel values of the second image are the same. The processor 340 may correct the brightness of the first image by applying the obtained data to the first image. However, it is not limited to this. For example, the processor 340 may obtain data for converting the pixel values of the second area so that the average of the pixel values of the first area and the average of the pixel values of the second image are the same. The processor 340 may correct the brightness of the second image by applying the obtained data to the second image.

In the above-described examples, examples of correcting the properties (and brightness) of the first image and/or the second image are described, but are not limited thereto. In one embodiment, the processor 340 configures parameters related to settings of the first camera 321 and/or the second camera 322 such that the first image and the second image have the same exposure and/or white balance. (parameters) can be controlled (e.g., set, adjusted).

In one embodiment, the processor 340 may selectively perform an operation to correct the attributes of the image of FIG. 8. For example, when the first image and the second image are images acquired through the first camera 321 and the second camera 322, the processor 340 performs an operation of correcting the properties of the image of FIG. 8 ( and an operation of correcting brightness and/or controlling parameters related to settings of the first camera 321 and/or the second camera 322). If the first image and the second image are images previously acquired through the first camera 321 and the second camera 322 and stored in the memory 330, the processor 340 determines the properties of the image in FIG. The compensating operation may not be performed.

FIG. 10 is a diagram 1000 for explaining a method of providing an image according to an embodiment.

Although the operation of providing an image using the first and second images acquired through each of the first camera 321 and the second camera 322 has been described through FIGS. 6 to 9, the operation is not limited thereto. For example, the processor 340 may perform an operation of providing an image using three or more images acquired through each of three or more cameras.

Referring to FIG. 10 , in one embodiment, the processor 340 acquires a first image 1010 through the first camera 321 and acquires a second image 1020 through the second camera 322. and the third image 1030 can be acquired through the third camera 323. For example, the processor 340 may acquire the first image 1010, the second image 1020, and the third image 1030 substantially identically (or sequentially within a designated time).

In one embodiment, the processor 340 performs at least some of the operations described with reference to FIGS. 6 to 9 for the first image 1010, the second image 1020, and the third image 1030. By performing similar operations (hereinafter referred to as “image synthesis operations”) two or more times, a final image (eg, an image to be displayed through the display module 310) can be obtained. For example, the processor 340 may obtain the first composite image 1011 by performing an image synthesis operation on the first image 1010 and the second image 1020. The processor 340 may obtain the second composite image 1021 by performing an image synthesis operation on the second image 1020 and the third image 1030. The processor 340 may obtain the final composite image 1012 by performing an image synthesis operation on the first composite image 1011 and the second composite image 1021. However, it is not limited to this. For example, the processor 340 may obtain the first composite image 1011 by performing an image synthesis operation on the first image 1010 and the second image 1020. The processor 340 may obtain the final composite image 1012 by performing an image synthesis operation on the first composite image 1011 and the third image 1030.

FIG. 11 is a diagram for explaining a method of providing an image, according to an embodiment.

Referring to FIG. 11, in one embodiment, the processor 340 performs an image compositing operation on a plurality of images acquired through each of the plurality of cameras 321, 322, and 323 while the camera application is running. By performing this, the final composite image can be obtained. For example, while the camera application is running, the processor 340 performs an image synthesis operation for a plurality of images acquired through each of the plurality of cameras 321, 322, and 323 in a designated mode for acquiring images. By performing , the final composite image to be displayed through the display module 310 can be obtained.

In one embodiment, the processor 340 may acquire a plurality of images through each of the plurality of cameras 321, 322, and 323, regardless of the zoom factor input by the user. The processor 340 may obtain a final composite image to be displayed through the display module 310 by performing an image synthesis operation on the plurality of acquired images.

In one embodiment, after the final composite image is acquired, the processor 340 may enlarge or reduce the final composite image based on the zoom factor input by the user and display it through the display module 310. .

In one embodiment, when the zoom magnification is changed, the processor 340 may enlarge or reduce the final composite image based on a point within the final composite image and display it through the display module 310. For example, when the zoom magnification is 0.5x, the processor 340 determines that the first image acquired through the first camera 321 is the same as the captured scene, as shown by reference numeral 1101. The final composite image 1110 including the scene can be displayed through the display module 310. When the zoom factor is changed from 0.5x (0.5x) to 1x (1.0x), the processor 340 uses a point 1151 of the final composite image 1110 as a reference, as shown by reference numeral 1102. An enlarged image 1120 of the final composite image 1110 can be displayed through the display module 310. When the zoom magnification is changed from 1x (1.0x) to 3x (3.0x), the processor 340 uses a point 1151 of the final composite image 1110 as a reference, as shown by reference numeral 1103. An enlarged image 1130 of the image 1120 can be displayed through the display module 310. A point 1152 in the image 1120 at 1102 and a point 1153 in the image 1130 at 1103 are both a point 1151 within the final composite image 1110 at 1101. ) may be the same points.

In one embodiment, when displaying the final composite image 1110 by enlarging it, the processor 340 corresponds to the image displayed on the screen in the reduced final composite image and the reduced final composite image in some areas of the screen. An object representing an area can be displayed through the display module 310. For example, the processor 340, as shown by reference numeral 1102, displays the reduced final composite image 1150 and the image displayed on the screen within the reduced final composite image 1150 in some areas of the screen. Object 1151 representing the area corresponding to 1120 can be displayed through the display module 310. For example, the processor 340, as shown by reference numeral 1103, displays the reduced final composite image 1160 and the image displayed on the screen within the reduced final composite image 1160 in some areas of the screen. Object 1161 representing the area corresponding to 1130 can be displayed through the display module 310.

In one embodiment, the processor 340 may display an object for selecting a zoom factor through the display module 310. For example, the processor 340, as shown in reference numerals 1101 to 1103, selects a first object 1141 for selecting a zoom factor of 0.5x (0.5x) and a zoom factor of 1x (1.0x). An object 1140 including a second object 1142 for selecting and a third object 1143 for selecting a zoom factor of 3x (3.0x) may be displayed through the display module 310.

In one embodiment, when the zoom magnification is changed, the processor 340 displays the final composite image by enlarging or reducing it based on a point within the final composite image through the display module 310, thereby displaying the final composite image when the zoom factor is changed. , each of the images corresponding to each of the zoom magnifications can be smoothly converted and displayed through the display module 310.

In one embodiment, a method of providing an image in the electronic device 301 includes acquiring a first image through a first camera 321 and using a second camera having a narrower field of view than the first camera 321. It may include an operation of acquiring a second image through 322. The method includes a difference between the positions of the first camera 321 and the second camera 322 and/or a difference between the optical axes of the first camera 321 and the second camera 322. Based on the calibration information 331, the operation of determining a first area corresponding to the second image within the first image may be included. The method may include aligning the first image and the second image by performing matching on the first area and the second image. The method may include stitching the aligned first image and the second image. The method may include obtaining an image to be displayed through the display module 310 of the electronic device by performing an image enhancement operation using the stitched first image and the second image. The method may include updating the calibration information 331 based on information for aligning the first image and the second image.

In one embodiment, the operation of aligning the first image and the second image includes obtaining first feature points from the first area, and adding second feature points corresponding to the first feature points to the second image. It may include obtaining information for aligning the first image and the second image based on the positions of the first feature points and the positions of the second feature points.

In one embodiment, the operation of aligning the first image and the second image includes aligning the second image within the first image based on differences between the positions of the first feature points and the positions of the second feature points. It may include the operation of sorting.

In one embodiment, the information for aligning the first image and the second image may include information for positioning the second image within the first image and/or information for warping the second image. You can.

In one embodiment, the operation of updating the calibration information 331 is based on information for positioning the second image within the first image, and the first camera 321 and the second camera 322 An operation of updating the difference between the positions of and an operation of updating the difference between the optical axes of the first camera 321 and the second camera 322 based on information for warping for the second image. You can.

In one embodiment, the method includes, based on the first area and the second image, obtaining information for converting properties of the first image and based on the obtained information, converting the first image The method may further include correcting the first image so that its properties are the same as those of the second image.

In one embodiment, the method includes obtaining an average of pixel values of the first area and an average of the pixel values of the second image using histograms for the first image and the second image, the method comprising: Obtaining data for converting pixel values of the first area so that the average of the pixel values of the first area and the average of the pixel values of the second image are the same, and converting the obtained data to the first image. Additional actions to apply may be included.

In one embodiment, the method may further include performing upscaling on the first image so that the size of the first area is the same as the size of the second image.

In one embodiment, the operation of performing an image enhancement operation using the stitched first image and the second image includes SR for the stitched first image and the second image using a learned artificial intelligence model. It may include actions that perform actions.

In one embodiment, the method acquires an image to be displayed through the display module 310, and then changes the acquired image based on a point of the acquired image based on a user input that changes the zoom factor. The operation of enlarging or reducing may further be included.

Additionally, the data structure used in the above-described embodiments of this document can be recorded on a computer-readable recording medium through various means. The computer-readable recording media includes storage media such as magnetic storage media (eg, ROM, floppy disk, hard disk, etc.) and optical read media (eg, CD-ROM, DVD, etc.).

Claims (20)

In the electronic device 301,
display module 310;
A plurality of cameras including a first camera 321 and a second camera 322 having a narrower angle of view than the angle of view of the first camera 321;
Calibration including a difference between the positions of the first camera 321 and the second camera 322 and/or a difference between the optical axes of the first camera 321 and the second camera 322. Memory 330 for storing information; and
At least one processor 340 operatively connected to the display module 310, the plurality of cameras, and the memory 330,
The at least one processor 340,
Obtaining a first image through the first camera 321 and acquiring a second image through the second camera 322,
Based on the calibration information 331, determining a first area corresponding to a second image within the first image,
Aligning the first image and the second image by performing matching on the first area and the second image,
Stitching the aligned first image and the second image,
Obtaining an image to be displayed through the display module 310 by performing an image enhancement operation using the stitched first image and the second image, and
An electronic device (301) configured to update the calibration information (331) based on information for aligning the first image and the second image.
According to claim 1,
The at least one processor 340,
Obtaining first feature points from the first area,
Obtaining second feature points corresponding to the first feature points with the second image, and
An electronic device (301) configured to obtain information for aligning the first image and the second image based on the positions of the first feature points and the positions of the second feature points.
The method of claim 1 or 2,
The at least one processor 340,
An electronic device (301) configured to align the second image within the first image based on a difference between the positions of the first feature points and the positions of the second feature points.
The method according to any one of claims 1 to 3,
Information for aligning the first image and the second image is:
An electronic device (301) including information for positioning the second image within the first image and/or information for warping the second image.
The method according to any one of claims 1 to 4,
The at least one processor 340,
update the difference between the positions of the first camera 321 and the second camera 322 based on information for positioning the second image within the first image, and
An electronic device (301) configured to update the difference between the optical axes of the first camera (321) and the second camera (322) based on information for warping of the second image.
The method according to any one of claims 1 to 5,
The at least one processor 340,
Based on the first area and the second image, obtain information for converting an attribute of the first image, and
Based on the obtained information, the electronic device (301) is configured to correct the first image so that the properties of the first image are the same as the properties of the second image.
The method according to any one of claims 1 to 6,
The at least one processor 340,
Using histograms for the first image and the second image, obtain the average of pixel values of the first area and the average of the pixel values of the second image,
Obtaining data for converting the pixel values of the first area so that the average of the pixel values of the first area and the average of the pixel values of the second image are the same, and
An electronic device (301) configured to apply the acquired data to the first image.
The method according to any one of claims 1 to 7,
The at least one processor 340,
The electronic device 301 configured to perform upscaling on the first image such that the size of the first area is the same as the size of the second image.
The method according to any one of claims 1 to 8,
The at least one processor 340,
An electronic device (301) configured to perform an SR operation on the stitched first image and the second image using a learned artificial intelligence model.
The method according to any one of claims 1 to 9,
The at least one processor 340,
After acquiring an image to be displayed through the display module 310, an electronic device configured to enlarge or reduce the acquired image based on a point of the acquired image based on a user input that changes the zoom factor ( 301).
In a method of providing an image from an electronic device 301,
Obtaining a first image through a first camera 321 and acquiring a second image through a second camera 322 having a narrower field of view than that of the first camera 321;
Calibration information 331 including a difference between the positions of the first camera 321 and the second camera 322 and/or a difference between the optical axes of the first camera 321 and the second camera 322 ) based on, determining a first area corresponding to a second image within the first image;
Aligning the first image and the second image by performing matching on the first area and the second image;
Stitching the aligned first image and the second image;
Obtaining an image to be displayed through the display module 310 of the electronic device 301 by performing an image enhancement operation using the stitched first image and the second image; and
A method comprising updating the calibration information 331 based on information for aligning the first image and the second image.
According to claim 11,
The operation of aligning the first image and the second image is,
Obtaining first feature points from the first area;
Obtaining second feature points corresponding to the first feature points from the second image; and
A method comprising obtaining information for aligning the first image and the second image based on the positions of the first feature points and the positions of the second feature points.
The method of claim 11 or 12,
The operation of aligning the first image and the second image is,
A method comprising aligning the second image within the first image based on a difference between the positions of the first feature points and the positions of the second feature points.
The method according to any one of claims 11 to 13,
Information for aligning the first image and the second image is:
A method comprising information for positioning the second image within the first image and/or information for warping the second image.
The method according to any one of claims 11 to 14,
The operation of updating the calibration information 331 is:
updating the difference between the positions of the first camera 321 and the second camera 322 based on information for locating the second image within the first image; and
A method comprising updating the difference between the optical axes of the first camera 321 and the second camera 322 based on information for warping of the second image.
The method according to any one of claims 11 to 15,
Obtaining information for converting properties of the first image based on the first area and the second image; and
Based on the obtained information, the method further includes correcting the first image so that the properties of the first image are the same as the properties of the second image.
The method according to any one of claims 11 to 16,
Obtaining an average of pixel values of the first area and an average of pixel values of the second image using histograms for the first image and the second image;
acquiring data for converting pixel values of the first area so that the average of the pixel values of the first area and the average of the pixel values of the second image are the same; and
The method further includes applying the acquired data to the first image.
The method according to any one of claims 11 to 17,
The method further includes performing upscaling on the first image so that the size of the first area is the same as the size of the second image.
The method according to any one of claims 11 to 18,
The operation of performing an image enhancement operation using the stitched first image and the second image includes,
A method comprising performing an SR operation on the stitched first image and the second image using a learned artificial intelligence model.
The method according to any one of claims 11 to 19,
After acquiring the image to be displayed through the display module 310, based on a user input that changes the zoom factor, further includes the operation of enlarging or reducing the acquired image based on a point of the acquired image. How to.

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