Patent Publication Number: US-2023154132-A1

Title: Method for providing image and electronic device supporting the same

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of International Application No. PCT/KR2022/011711 designating the United States, filed on Aug. 5, 2022, in the Korean Intellectual Property Receiving Office and claiming priority to Korean Patent Application No. 10-2021-0104654, filed on Aug. 9, 2021, in the Korean Intellectual Property Office, the disclosures of which are incorporated by reference herein in their entireties. 
    
    
     BACKGROUND 
     Field 
     The disclosure relates to a method for providing an image and an electronic device for supporting the same. 
     Description of Related Art 
     With the release of a display having a high resolution (e.g., ultra-high definition (UHD)), interest in super-resolution techniques is increasing. The super-resolution technique is a method for converting a low-resolution image into a high-resolution image. 
     In the super-resolution technique, a plurality of images may be used or a single image may be used. For example, the super-resolution technique includes a pixel shift method for obtaining a high-resolution image from a plurality of low-resolution images obtained by a camera while moving the camera (e.g., an image sensor) at intervals of one pixel or half a pixel and a single image super-resolution (SISR) (also referred to as “single frame super-resolution (SFSR)”) method for obtaining one high-resolution image from one low-resolution image. 
     The pixel shift method requires a component for moving a camera at intervals of one pixel or half a pixel from the aspect of hardware and may thus be difficult to implement in an electronic device (e.g., a smartphone). The SISR method uses one low-resolution image as an input may thus have limitation in obtaining an image having a high resolution from the one low-resolution image. 
     SUMMARY 
     Embodiments of the disclosure relate to a method for providing an image and an electronic device for supporting the same, which align a plurality of images obtained through a camera of an electronic device, and obtains a high-resolution image using an artificial intelligence model, based on the plurality of aligned images and a reference image obtained from the plurality of aligned images. 
     Technical aspects to be achieved in the disclosure are not limited to the technical aspects mentioned above, and other technical aspects not mentioned will be clearly understood by those skilled in the art from the following description. 
     An electronic device according to various example embodiments of the disclosure may include: a camera module comprising a camera, a memory, and at least one processor electrically connected to the camera module and the memory, wherein the at least one processor may be configured to: successively obtain a plurality of first images through the camera module, align the plurality of first images, obtain a reference image based on the plurality of aligned first images, and obtain a second image using an artificial intelligence model based on the plurality of aligned first images and the reference image. 
     A method for providing an image by an electronic device according to various example embodiments of the disclosure may include: successively obtaining a plurality of first images through a camera module of the electronic device, aligning the plurality of first images, obtaining a reference image based on the plurality of aligned first images, and obtaining a second image using an artificial intelligence model based on the plurality of aligned first images and the reference image. 
     A method for providing an image and an electronic device for supporting the same according to various example embodiments of the disclosure may align a plurality of images obtained through a camera of an electronic device and may obtain a high-resolution image using an artificial intelligence model, based on the plurality of aligned images and a reference image obtained from the plurality of aligned images. 
     Further, a method for providing an image and an electronic device for supporting the same according to various example embodiments of the disclosure may perform training using a low-resolution image and a high-resolution image obtained through an actual camera, thereby improving an artificial intelligence model for providing a super-resolution technique. 
     In addition, a method for providing an image and an electronic device for supporting the same according to various example embodiments of the disclosure may obtain a high-resolution image in which high-frequency domain data is reconstructed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other aspects, features and advantages of certain embodiments of the present disclosure will be more apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which: 
         FIG.  1    is a block diagram of an electronic device in a network environment according to various embodiments; 
         FIG.  2    is a block diagram illustrating a camera module according to various embodiments; 
         FIG.  3    is a block diagram illustrating an example configuration of an electronic device according to various embodiments; 
         FIG.  4    is a block diagram illustrating example components for performing an operation of providing an image according to various embodiments; 
         FIG.  5    is a diagram illustrating an example method for aligning a plurality of images according to various embodiments; 
         FIG.  6    is a flowchart illustrating an example method for providing an image according to various embodiments; 
         FIG.  7    is a flowchart illustrating an example method for providing an image according to various embodiments; 
         FIG.  8 A  is a diagram illustrating an example method for aligning a plurality of images according to various embodiments; and 
         FIG.  8 B  is a diagram illustrating an example method for aligning a plurality of images according to various embodiments. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    is a block diagram illustrating an electronic device  101  in a network environment  100  according to various embodiments. 
     Referring to  FIG.  1   , the electronic device  101  in the network environment  100  may communicate with an electronic device  102  via a first network  198  (e.g., a short-range wireless communication network), or at least one of an electronic device  104  or a server  108  via a second network  199  (e.g., a long-range wireless communication network). According to an embodiment, the electronic device  101  may communicate with the electronic device  104  via the server  108 . According to an embodiment, the electronic device  101  may include a processor  120 , memory  130 , an input module  150 , a sound output module  155 , a display module  160 , an audio module  170 , a sensor module  176 , an interface  177 , a connecting terminal  178 , a haptic module  179 , a camera module  180 , a power management module  188 , a battery  189 , a communication module  190 , a subscriber identification module (SIM)  196 , or an antenna module  197 . In some embodiments, at least one of the components (e.g., the connecting terminal  178 ) may be omitted from the electronic device  101 , or one or more other components may be added in the electronic device  101 . In some embodiments, some of the components (e.g., the sensor module  176 , the camera module  180 , or the antenna module  197 ) may be implemented as a single component (e.g., the display module  160 ). 
     The processor  120  may execute, for example, software (e.g., a program  140 ) to control at least one other component (e.g., a hardware or software component) of the electronic device  101  coupled with the processor  120 , and may perform various data processing or computation. According to one embodiment, as at least part of the data processing or computation, the processor  120  may store a command or data received from another component (e.g., the sensor module  176  or the communication module  190 ) in volatile memory  132 , process the command or the data stored in the volatile memory  132 , and store resulting data in non-volatile memory  134 . According to an embodiment, the processor  120  may include a main processor  121  (e.g., a central processing unit (CPU) or an application processor (AP)), or an auxiliary processor  123  (e.g., a graphics processing unit (GPU), a neural processing unit (NPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is operable independently from, or in conjunction with, the main processor  121 . For example, when the electronic device  101  includes the main processor  121  and the auxiliary processor  123 , the auxiliary processor  123  may be adapted to consume less power than the main processor  121 , or to be specific to a specified function. The auxiliary processor  123  may be implemented as separate from, or as part of the main processor  121 . 
     The auxiliary processor  123  may control, for example, at least some of functions or states related to at least one component (e.g., the display module  160 , the sensor module  176 , or the communication module  190 ) among the components of the electronic device  101 , instead of the main processor  121  while the main processor  121  is in an inactive (e.g., sleep) state, or together with the main processor  121  while the main processor  121  is in an active (e.g., executing an application) state. According to an embodiment, the auxiliary processor  123  (e.g., an image signal processor or a communication processor) may be implemented as part of another component (e.g., the camera module  180  or the communication module  190 ) functionally related to the auxiliary processor  123 . According to an embodiment, the auxiliary processor  123  (e.g., the neural processing unit) may include a hardware structure specified for artificial intelligence model processing. An artificial intelligence model may be generated by machine learning. Such learning may be performed, e.g., by the electronic device  101  where the artificial intelligence model is performed or via a separate server (e.g., the server  108 ). Learning algorithms may include, but are not limited to, e.g., supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. The artificial intelligence model may include a plurality of artificial neural network layers. The artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-network or a combination of two or more thereof but is not limited thereto. The artificial intelligence model may, additionally or alternatively, include a software structure other than the hardware structure. 
     The memory  130  may store various data used by at least one component (e.g., the processor  120  or the sensor module  176 ) of the electronic device  101 . The various data may include, for example, software (e.g., the program  140 ) and input data or output data for a command related thereto. The memory  130  may include the volatile memory  132  or the non-volatile memory  134 . 
     The program  140  may be stored in the memory  130  as software, and may include, for example, an operating system (OS)  142 , middleware  144 , or an application  146 . 
     The input module  150  may receive a command or data to be used by another component (e.g., the processor  120 ) of the electronic device  101 , from the outside (e.g., a user) of the electronic device  101 . The input module  150  may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a 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. The speaker may be used for general purposes, such as playing multimedia or playing record. The receiver may be used for receiving incoming calls. According to an embodiment, the receiver may be implemented as separate from, or as part of the speaker. 
     The display module  160  may visually provide information to the outside (e.g., a user) of the electronic device  101 . The display module  160  may include, for example, a display, a hologram device, or a projector and control circuitry to control a corresponding one of the display, hologram device, and projector. According to an embodiment, the display module  160  may include a touch sensor adapted to detect a touch, or a pressure sensor adapted to measure the intensity of force incurred by the touch. 
     The audio module  170  may convert a sound into an electrical signal and vice versa. According to an embodiment, the audio module  170  may obtain the sound via the input module  150 , or output the sound via the sound output module  155  or an external electronic device (e.g., an electronic device  102  (e.g., a speaker or a headphone)) directly or wirelessly coupled with the electronic device  101 . 
     The sensor module  176  may detect an operational state (e.g., power or temperature) of the electronic device  101  or an environmental state (e.g., a state of a user) external to the electronic device  101 , and then generate an electrical signal or data value corresponding to the detected state. According to an embodiment, the sensor module  176  may include, for example, a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. 
     The interface  177  may support one or more specified protocols to be used for the electronic device  101  to be coupled with the external electronic device (e.g., the electronic device  102 ) directly or wirelessly. According to an embodiment, the interface  177  may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, a secure digital (SD) card interface, or an audio interface. 
     A connecting terminal  178  may include a connector via which the electronic device  101  may be physically connected with the external electronic device (e.g., the electronic device  102 ). According to an embodiment, the connecting terminal  178  may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector). 
     The haptic module  179  may convert an electrical signal into a mechanical stimulus (e.g., a vibration or a movement) or electrical stimulus which may be recognized by a user via his tactile sensation or kinesthetic sensation. According to an embodiment, the haptic module  179  may include, for example, a motor, a piezoelectric element, or an electric stimulator. 
     The camera module  180  may capture a still image or moving images. According to an embodiment, the camera module  180  may include one or more lenses, image sensors, image signal processors, or flashes. 
     The power management module  188  may manage power supplied to the electronic device  101 . According to one embodiment, the power management module  188  may be implemented as at least 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 an embodiment, the battery  189  may include, for example, a primary cell which is not rechargeable, a secondary cell which is rechargeable, or a fuel cell. 
     The communication module  190  may support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device  101  and the external electronic device (e.g., the electronic device  102 , the electronic device  104 , or the server  108 ) and performing communication via the established communication channel. The communication module  190  may include one or more communication processors that are operable independently from the processor  120  (e.g., the application processor (AP)) and supports a direct (e.g., wired) communication or a wireless communication. According to an embodiment, the communication module  190  may include 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., a local area network (LAN) communication module or a power line communication (PLC) module). A corresponding one of these communication modules may communicate with the external electronic device  104  via the first network  198  (e.g., a short-range communication network, such as Bluetooth™, wireless-fidelity (Wi-Fi) direct, or infrared data association (IrDA)) or the second network  199  (e.g., a long-range communication network, such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or wide area network (WAN)). These various types of communication modules may be implemented as a single component (e.g., a single chip), or may be implemented as multi components (e.g., multi chips) separate from each other. The wireless communication module  192  may identify or authenticate the electronic device  101  in a communication network, such as the first network  198  or the second network  199 , using subscriber information (e.g., international mobile subscriber identity (IMSI)) stored in the subscriber identification module  196 . 
     The wireless communication module  192  may support a 5G network, after a 4G network, and next-generation communication technology, e.g., new radio (NR) access technology. The NR access technology may support enhanced mobile broadband (eMBB), massive machine type communications (mMTC), or ultra-reliable and low-latency communications (URLLC). The wireless communication module  192  may support a high-frequency band (e.g., the mmWave band) to achieve, e.g., a high data transmission rate. The wireless communication module  192  may support various technologies for securing performance on a high-frequency band, such as, e.g., beamforming, massive multiple-input and multiple-output (massive MIMO), full dimensional MIMO (FD-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., the electronic device  104 ), or a network system (e.g., the second network  199 ). According to an embodiment, the wireless communication module  192  may support a peak data rate (e.g., 20 Gbps or more) for implementing eMBB, loss coverage (e.g., 164 dB or less) for implementing mMTC, or U-plane latency (e.g., 0.5 ms or less for each of downlink (DL) and uplink (UL), or a round trip of 1 ms or less) for implementing URLLC. 
     The antenna module  197  may transmit or receive a signal or power to or from the outside (e.g., the external electronic device) of the electronic device  101 . According to an embodiment, the antenna module  197  may include an antenna including a radiating element composed of a conductive material or a conductive pattern formed in or on a substrate (e.g., a printed circuit board (PCB)). According to an embodiment, the antenna module  197  may include a plurality of antennas (e.g., array antennas). In such a case, at least one antenna appropriate for a communication scheme used in the communication network, such as the first network  198  or the second network  199 , may be selected, for example, by the communication module  190  from the plurality of antennas. The signal or the power may then be transmitted or received between the communication module  190  and the external electronic device via the selected at least one antenna. According to an embodiment, another component (e.g., a radio frequency integrated circuit (RFIC)) other than the radiating element may be additionally formed as part of the antenna module  197 . 
     According to various embodiments, the antenna module  197  may form a mmWave antenna module. According to an embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on a first surface (e.g., the bottom surface) of the printed circuit board, or adjacent to the first surface and capable of supporting a designated high-frequency band (e.g., the mmWave band), and a plurality of antennas (e.g., array antennas) disposed on a second surface (e.g., the top or a side surface) of the printed circuit board, or adjacent to the second surface and capable of transmitting or receiving signals of the designated high-frequency band. 
     At least some of the above-described components may be coupled mutually and communicate signals (e.g., commands or data) therebetween via an inter-peripheral communication scheme (e.g., a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)). 
     According to an embodiment, commands or data may be transmitted or received between the electronic device  101  and the external electronic device  104  via the server  108  coupled with the second network  199 . Each of the external electronic devices  102  or  104  may be a device of a same type as, or a different type, from the electronic device  101 . According to an embodiment, all or some of operations to be executed at the electronic device  101  may be executed at one or more of the external electronic devices  102 ,  104 , or  108 . For example, if the electronic device  101  should perform a function or a service automatically, or in response to a request from a user or another device, the electronic device  101 , instead of, or in addition to, executing the function or the service, may request the one or more external electronic devices to perform at least part of the function or the service. The one or more external electronic devices receiving the request may perform the at least part of the function or the service requested, or an additional function or an additional service related to the request, and transfer an outcome of the performing to the electronic device  101 . The electronic device  101  may provide the outcome, with or without further processing of the outcome, as at least part of a reply to the request. To that end, a cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device  101  may provide ultra low-latency services using, e.g., distributed computing or mobile edge computing. In another embodiment, the external electronic device  104  may include an internet-of-things (IoT) device. The server  108  may be an intelligent server using machine learning and/or a neural network. According to an embodiment, the external electronic device  104  or the 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 or IoT-related technology. 
     The electronic device according to various embodiments may be one of various types of electronic devices. The electronic devices may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance. According to an embodiment of the disclosure, the electronic devices are not limited to those described above. 
     It should be appreciated that various embodiments of the disclosure and the terms used therein are not intended to limit the technological features set forth herein to particular embodiments and include various changes, equivalents, or replacements for a corresponding embodiment. With regard to the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It is to be understood that a singular form of a noun corresponding to an item may include one or more of the things, unless the relevant context clearly indicates otherwise. As used herein, each of such phrases as “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 “at least one of A, B, or C”, may include any one of, or all possible combinations of the items enumerated together in a corresponding one of the phrases. As used herein, such terms as “1st” and “2nd”, or “first” and “second” may be used to simply distinguish a corresponding component from another, and does not limit the components in other aspect (e.g., importance or order). It is to be understood that if an element (e.g., a first element) is referred to, with or without the term “operatively” or “communicatively”, as “coupled with”, “coupled to”, “connected with”, or “connected to” another element (e.g., a second element), it means that the element may be coupled with the other element directly (e.g., wiredly), wirelessly, or via a third element. 
     As used in connection with various embodiments of the disclosure, the term “module” may include a unit implemented in hardware, software, or firmware, and may interchangeably be used with other terms, for example, “logic”, “logic block”, “part”, or “circuitry”. A module may be a single integral component, or a minimum unit or part thereof, adapted to perform one or more functions. For example, according to an embodiment, the module may be implemented in a form of an application-specific integrated circuit (ASIC). 
     Various embodiments as set forth herein may be implemented as software (e.g., the program  140 ) including one or more instructions that are stored in a storage medium (e.g., internal memory  136  or external memory  138 ) that is readable by a machine (e.g., the electronic device  101 ). For example, a processor (e.g., the processor  120 ) of the machine (e.g., the electronic device  101 ) may invoke at least one of the one or more instructions stored in the storage medium, and execute it. This allows the machine to be operated to perform at least one function according to the at least one instruction invoked. The one or more instructions may include a code generated by a complier or a code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Wherein, the term “non-transitory” simply means that the storage medium is a tangible device, and does not include a signal (e.g., an electromagnetic wave), but this term does not differentiate between where data is semi-permanently stored in the storage medium and where the data is temporarily stored in the storage medium. 
     According to an embodiment, a method according to various embodiments of the disclosure may be included and provided in a computer program product. The computer program product may be traded as a product between a seller and a buyer. 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 be distributed (e.g., downloaded or uploaded) online via an application store (e.g., PlayStore™), or between two user devices (e.g., smart phones) directly. If distributed online, at least part of the computer program product may be temporarily generated or at least temporarily stored in the machine-readable storage medium, such as memory of the manufacturer&#39;s server, a server of the application store, or a relay server. 
     According to various embodiments, each component (e.g., a module or a program) of the above-described components may include a single entity or multiple entities, and some of the multiple entities may be separately disposed in different components. According to various embodiments, one or more of the above-described components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) may be integrated into a single component. In such a case, the integrated component may still perform one or more functions of each of the plurality of components in the same or similar manner as they are performed by a corresponding one of the plurality of components before the integration. According to various embodiments, operations performed by the module, the program, or another component may be carried out sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations may be executed in a different order or omitted, or one or more other operations may be added. 
       FIG.  2    is a block diagram  200  illustrating the camera module  180  according to various embodiments. 
     Referring to  FIG.  2   , the camera module  180  may include a lens assembly  221 , a flash  222 , an image sensor  223 , an image stabilizer  224 , memory  225  (e.g., buffer memory), or an image signal processor  226   
     The lens assembly  221  may collect light emitted or reflected from an object whose image is to be taken. The lens assembly  221  may include one or more lenses. According to an embodiment, the camera module  180  may include a plurality of lens assemblies  221 . In such a 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  221  may have the same lens attribute (e.g., view angle, focal length, auto-focusing, f number, or optical zoom), or at least one lens assembly may have one or more lens attributes different from those of another lens assembly. The lens assembly  221  may include, for example, a wide-angle lens or a telephoto lens. 
     The flash  222  may emit light that is used to reinforce light reflected from an object. According to an embodiment, the flash  222  may include one or more light emitting diodes (LEDs) (e.g., a red-green-blue (RGB) LED, a white LED, an infrared (IR) LED, or an ultraviolet (UV) LED) or a xenon lamp. The image sensor  223  may obtain an image corresponding to an object by converting light emitted or reflected from the object and transmitted via the lens assembly  221  into an electrical signal. 
     According to an embodiment, the image sensor  223  may include one selected from image sensors having different attributes, such as a RGB sensor, a black-and-white (BW) sensor, an IR sensor, or a UV sensor, a plurality of image sensors having the same attribute, or a plurality of image sensors having different attributes. Each image sensor included in the image sensor  223  may be implemented using, for example, a charged coupled device (CCD) sensor or a complementary metal oxide semiconductor (CMOS) sensor. 
     The image stabilizer  224  may move the image sensor  223  or at least one lens included in the lens assembly  221  in a particular direction, or control an operational attribute (e.g., adjust the read-out timing) of the image sensor  223  in response to the movement of the camera module  180  or the electronic device  101  including the camera module  180 . This allows compensating for at least part of a negative effect (e.g., image blurring) by the movement on an image being captured. According to an embodiment, the image stabilizer  224  may sense such a movement by the camera module  180  or the electronic device  101  using a gyro sensor (not shown) or an acceleration sensor (not shown) disposed inside or outside the camera module  180 . According to an embodiment, the image stabilizer  224  may be implemented, for example, as an optical image stabilizer. 
     The memory  225  may store, at least temporarily, at least part of an image obtained via the image sensor  223  for a subsequent image processing task. For example, if image capturing is delayed due to shutter lag or multiple images are quickly captured, a raw image obtained (e.g., a Bayer-patterned image, a high-resolution image) may be stored in the memory  225 , and its corresponding copy image (e.g., a low-resolution image) may be previewed via the display module  160 . Thereafter, if a specified condition is met (e.g., by a user&#39;s input or system command), at least part of the raw image stored in the memory  225  may be obtained and processed, for example, by the image signal processor  226 . According to an embodiment, the memory  225  may be configured as at least part of the memory  130  or as a separate memory that is operated independently from the memory  130 . 
     The image signal processor  226  may perform one or more image processing with respect to an image obtained via the image sensor  223  or an image stored in the memory  225 . The one or more image processing may include, for example, depth map generation, three-dimensional (3D) modeling, panorama generation, feature point extraction, image synthesizing, or image compensation (e.g., noise reduction, resolution adjustment, brightness adjustment, blurring, sharpening, or softening). Additionally or alternatively, the image signal processor  226  may perform control (e.g., exposure time control or read-out timing control) with respect to at least one (e.g., the image sensor  223 ) of the components included in the camera module  180 . An image processed by the image signal processor  226  may be stored back in the memory  225  for further processing, or may be provided to an external component (e.g., the memory  130 , the display module  160 , the electronic device  102 , the electronic device  104 , or the server  108 ) outside the camera module  180 . According to an embodiment, the image signal processor  226  may be configured as at least part of the processor  120 , or as a separate processor that is operated independently from the processor  120 . If the image signal processor  226  is configured as a separate processor from the processor  120 , at least one image processed by the image signal processor  226  may be displayed, by the processor  120 , via the display module  160  as it is or after being further processed. 
     According to an embodiment, the electronic device  101  may include a plurality of camera modules  180  having different attributes or functions. In such a case, at least one of the plurality of camera modules  180  may form, for example, a wide-angle camera and at least another of the plurality of camera modules  180  may form a telephoto camera. Similarly, at least one of the plurality of camera modules may form, for example, a front camera and at least another of the plurality of camera modules may form a rear camera. 
       FIG.  3    is a block diagram illustrating an example configuration of an electronic device  101  according to various embodiments. 
     Referring to  FIG.  3   , in an embodiment, the electronic device  101  may include a display  210 , a camera module (e.g., a camera module including a camera)  220 , a memory  230 , and/or a processor (e.g., a processor including processing circuitry)  240 . 
     In an embodiment, the display  210  may be included in the display module  160  of  FIG.  1   . 
     In an embodiment, the display  210  may display an image obtained through the camera module  220 . For example, the display  210  may display a plurality of images successively obtained through the camera module  220 . In another example, when a plurality of images successively obtained through the camera module  220  is processed, the display  210  may display the images obtained through processing. 
     In an embodiment, the camera module  220  may be included in the camera module  180  of  FIG.  1    and  FIG.  2   . 
     In an embodiment, the camera module  220  may include a camera and successively obtain a plurality of images. The plurality of successively obtained images may be images having different views (e.g., images having mutual parallax). 
     In an embodiment, the memory  230  may be included in the memory  130  of  FIG.  1   . 
     In an embodiment, the memory  230  may include components for performing at least part of an operation of providing an image. 
     In an embodiment, the processor  240  may be included in the processor  120  of  FIG.  1   . 
     In an embodiment, the processor  240  may include various processing circuitry and control the overall operation of providing the image. In an embodiment, the processor  240  may include one or more processors for performing the operation of providing the image. 
     In an embodiment, although  FIG.  3    shows that the electronic device  101  includes the display  210 , the camera module  220 , the memory  230 , and/or the processor  240 , but the disclosure is not limited thereto. For example, the electronic device  101  may not include the display  210  according to an embodiment. In another example, the electronic device  101  may further include at least one component (e.g., the communication module  190 ) among the components of the electronic device  101  illustrated in  FIG.  1   . 
       FIG.  4    is a block diagram  400  illustrating example components for performing an operation of providing an image according to various embodiments. 
     Referring to  FIG.  4   , in an embodiment, the components for performing the operation of providing the image may include a crop module (e.g., a crop module including various processing circuitry and/or executable program instructions)  310 , an upscaling module (e.g., an upscaling module including various processing circuitry and/or executable program instructions)  320 , an alignment module (e.g., an alignment module including various processing circuitry and/or executable program instructions)  330 , a weighted average module (e.g., a weighted average module including various processing circuitry and/or executable program instructions)  340 , and/or an artificial intelligence model (e.g., an artificial intelligence model including various processing circuitry and/or executable program instructions)  231 . In an embodiment, the crop module  310 , the upscaling module  320 , the alignment module  330 , the weighted average module  340 , and/or the artificial intelligence model  231  may be stored in a memory  230 . 
     In an embodiment, the artificial intelligence model  231  may include a concatenation module (e.g., a concatenation module including various processing circuitry and/or executable program instructions)  350 , a single frame super-resolution (SFSR) module (e.g., a SFSR module including various processing circuitry and/or executable program instructions)  360 , and/or a synthesis module (e.g., a synthesis module including various processing circuitry and/or executable program instructions)  370 . However, the disclosure is not limited thereto, and at least one of the concatenation module  350 , the single frame super-resolution (SFSR) module  360 , and the synthesis module  370  may be configured using a designated algorithm without using artificial intelligence. 
     In an embodiment, at least one of the crop module  310 , the upscaling module  320 , the alignment module  330 , or the weighted average module  340  may be included in the artificial intelligence model  231 . 
     In an embodiment, the crop module  310  may crop a plurality of images (which may be referred to hereinafter as “a plurality of first images”) successively obtained through a camera module  220 . In an embodiment, when a zoom input (e.g., a zoom-in input) associated with the camera module  220  is received from a user, the crop module  310  may crop the plurality of first images, based on zoom magnification associated with the camera module  220 . For example, when an input to increase the zoom magnification twice from a zoom magnification of 1.0x to a zoom magnification of 2.0x, the crop module  310  may crop a ¼area of each image of the plurality of first images, obtained through the entire area of an image sensor of the camera module  220  (or an area of the image sensor corresponding to a currently displayed image), based on the center of the image (or the center of the image being currently displayed through a display  210 ). In an embodiment, the crop module  310  may transmit a plurality of cropped first images (hereinafter, referred to as “a plurality of cropped first images”) to the upscaling module  320 . 
     In an embodiment, the upscaling module  312  may perform an operation of upscaling the plurality of cropped first images. 
     In an embodiment, the upscaling module  320  may upscale the plurality of cropped first images into a plurality of images (which may be referred to hereinafter as “a plurality of upscaled first images”) having a greater size than the size of the plurality of cropped first images. 
     In an embodiment, the upscaling module  320  may perform the operation of upscaling the plurality of cropped first images, based on the zoom magnification associated with the camera module  220 . For example, when the ¼area of each image of the plurality of first images based on the center of the image is cropped based on receiving the input to increase the zoom magnification twice from a zoom magnification of 1.0x to a zoom magnification of 2.0x, the upscaling module  320  may upscale the plurality of cropped first images having a first size into the plurality of upscaled first images having a second size four times (e.g., twice in width and twice in length) greater than the first size. 
     In an embodiment, the upscaling module  320  may perform the operation of upscaling the plurality of cropped first images using various algorithms For example, the upscaling module  320  may perform the operation of upscaling the plurality of cropped first images using a nearest neighbor algorithm, a bicubic algorithm, or a bilinear algorithm. However, an algorithm used by the upscaling module  320  to perform the operation of upscaling the plurality of cropped first images is not limited to the foregoing algorithms 
     In an embodiment, the upscaling module  320  may not be included in a processor  240 , in which case an upscaling operation may be performed in the artificial intelligence model  231  (e.g., the SFSR module  360 ). 
     In an embodiment, the alignment module  330  may align the plurality of upscaled first images. 
     In an embodiment, the alignment module  330  may align, based on one image among the plurality of upscaled first images (e.g., the position of the one image among the plurality of upscaled first images), images (e.g., the positions of the images) other than the one image as a reference among the plurality of upscaled first images. Hereinafter, a method in which the alignment module  330  aligns the plurality of upscaled first images will be described in greater detail below with reference to  FIG.  5   . 
       FIG.  5    is a diagram  500  illustrating an example method for aligning a plurality of images according to various embodiments. 
     Referring to  FIG.  5   , in an embodiment, reference numeral  501  may denote a plurality of upscaled first images. For example, the plurality of upscaled first images may include image  1   511 , image  2   512 , image  3   513 , and image  4   514 . Reference numeral  501  shows four upscaled first images as the plurality of upscaled first images, but is not limited thereto. In an embodiment, referring to reference numeral  501  and reference numeral  502 , image  1   511 , image  2   512 , image  3   513 , and image  4   514  may be images each having a Bayer pattern. For example, in image  1   511 , a first pixel  511 - 1  may be a pixel corresponding to red of RGB, a second pixel  511 - 2  and a third pixel  511 - 3  may be pixels corresponding to green of RGB, and a fourth pixel  511 - 4  may be a pixel corresponding to blue of RGB. However, the disclosure is not limited thereto, and the plurality of upscaled first images may be images having RGB data. 
     In an embodiment, the alignment module  330  may obtain information about a movement of the camera module  220  while successively obtaining a plurality of first images through the camera module  220 . For example, the alignment module  330  may obtain (e.g., calculate) differences between a position of the camera module  220  at which a first image among the plurality of first images (e.g., an image obtained first among the plurality of first images) is obtained and positions of the camera module  220  at which images after the first image (e.g., images obtained after the first image is obtained) are obtained while successively obtaining the plurality of first images through the camera module  220 . In an embodiment, the alignment module  330  may obtain the information about the movement of the camera module  220  by comparing pixel values of the first image and pixel values of each of the images after the first image while successively obtaining the plurality of first images through the camera module  220 . 
     In an embodiment, the alignment module  330  may align the plurality of scaled first images, based on the obtained information about the movement of the camera module  220 . For example, as shown by reference numeral  502 , the alignment module  330  may align, based on one image (e.g., the image  511 ) among the plurality of scaled first images, images (e.g., the images  512 ,  513 , and  514 ) other than the one image as a reference. In an embodiment, the alignment module  330  may shift the positions of the other images, based on the position of the one image as the reference among the plurality of scaled first images, based on the obtained information about the movement of the camera module  220 , thereby aligning the plurality of upscaled first images (hereinafter, the one image as the reference and the other images after the alignment are referred to as “a plurality of aligned first images”). In an embodiment, the alignment module  330  may transmit the plurality of aligned first images to the artificial intelligence model  231  (e.g., the concatenation module  350 ). 
     In an embodiment, the weighted average module  340  may average the plurality of aligned first images in view of a weight. For example, the weighted averaging module  340  may assign a weight to each of the plurality of aligned first images, based on the quality of each of the plurality of aligned first images (e.g., the degree to which each of the plurality of aligned first images is blurred and/or the peak signal-to-noise ratio (PSNR) of each of the plurality of aligned first images). Weights assigned to the plurality of aligned first images may be the same or different. 
     In an embodiment, the weighted average module  340  may average the plurality of aligned first images (e.g., pixel values of each of the plurality of aligned first images), based on the weights assigned to the plurality of aligned first images, thereby obtaining one image (hereinafter, referred to as a “reference image”). 
     In an embodiment, the reference image may be an image in which the strength of an original signal (e.g., a pattern of each of the plurality of aligned first images) is increased in a high-frequency region and the strength of noise is reduced, compared to the plurality of aligned first images. In an embodiment, the reference image may be an image including data of the high-frequency region that is not included in the plurality of aligned first images. In an embodiment, the reference image may be an image including less noise (e.g., a Moiré signal) than the plurality of aligned first images. In an embodiment, the reference image may be an image from which at least part of noise included in the plurality of aligned first images is removed. In an embodiment, the reference image may be an image generated based on at least a portion of each of the plurality of aligned first images. 
     In an embodiment, the weighted average module  340  may average all of the plurality of aligned first images or some images of the plurality of aligned first images in view of the weights. In an embodiment, the weighted average module  340  may transmit the reference image to the artificial intelligence model  231  (e.g., the concatenation module  350 ). 
     In an embodiment, the concatenation module  350  may obtain the plurality of aligned first images from the alignment module  330 , and may obtain the reference image from the weighted average module  340 . In an embodiment, the concatenation module  350  may convert the plurality of aligned first images and the reference image into one image. For example, the plurality of aligned first images and the reference image may each have a red channel, a green channel, and a blue channel The concatenation module  350  may obtain one image having the channels of the plurality of aligned first images and the channels of the reference image from the plurality of aligned first images and the reference image. 
     In an embodiment, the SFSR module  360  (also referred to as an “SFSR artificial intelligence model”) may output an image in which noise (e.g., a Moiré signal) is reduced (or removed) and data of the high-frequency region is restored as result data using the image converted from the plurality of aligned first images and the reference image as input data. For example, the SFSR module  360  may output an image in which each pixel to form the image has a red value, a green value, and a blue value, based on the one image having the channels of the plurality of aligned first images and the channels of the reference image, similarly to the foregoing pixel shifting. 
     In an embodiment, the SFSR module  360  may include an artificial intelligence model using, for example, very deep residual channel attention networks (RCAN). The RCAN may include a convolution layer for extracting a feature (e.g., a shallow feature) of input data, a skip connection (e.g., a long skip connection), a plurality of residual groups, and convolution layers for extracting a feature (e.g., a deep feature) from resulting data of the residual groups. Each residual group of the RCAN may include a plurality of channel attention blocks, a skip connection (e.g., a short skip connection), and a convolution layer, respectively. However, an artificial intelligence network used by the SFSR module  360  is not limited to the RCAN, and various artificial intelligence networks may be used. For example, the SFSR module  360  may use an SRCNN, an FSRCNN, an ESPCN, or a VDSR. 
     In an embodiment, the synthesis module  370  may obtain a final image (also referred to as a “second image”), based on the image output from the artificial intelligence model  231  and the reference image input from the weighted average module  340 . In an embodiment, the second image may be an image in which noise (e.g., a Moire signal) existing in the plurality of first images (e.g., aligned first images) or the reference image is reduced by cancelation and the data in the high-frequency region is amplified. 
     Although  FIG.  4    shows that the artificial intelligence model  231  include the SFSR module  360 , the disclosure is not limited thereto. In an embodiment, the artificial intelligence model  231  may include a denoising artificial intelligence model (e.g., a denoising CNN model) in addition to or in place of the SFSR module  360 . When the artificial intelligence model  231  uses the denoising artificial intelligence model  231 , noise may be removed from an image, thus obtaining an image having an improved image quality. 
     An electronic device according to various example embodiments of the disclosure may include a camera module including a camera, a memory, and at least one processor electrically connected to the camera module and the memory, wherein the at least one processor may be configured to successively obtain a plurality of first images through the camera module, align the plurality of first images, obtain a reference image based on the plurality of aligned first images, and obtain a second image using an artificial intelligence model based on the plurality of aligned first images and the reference image. 
     In various example embodiments, the at least one processor may be configured to obtain the reference image by performing a weighted average operation on the plurality of aligned first images. 
     In various example embodiments, the second image may have a higher resolution than a resolution of the plurality of first images. 
     In various example embodiments, the at least one processor may be configured to: crop the plurality of first images, upscale the plurality of cropped first images, and align the plurality of upscaled first images. 
     In various example embodiments, the at least one processor may be configured to: crop the plurality of first images based on receiving an input to increase a zoom magnification associated with the camera module, and upscale the plurality of cropped first images based on the zoom magnification. 
     In various example embodiments, the reference image may include an image in which a strength of an original signal is increased in a high-frequency region and a strength of noise is reduced compared to the plurality of upscaled first images. 
     In various example embodiments, the at least one processor may be configured to: obtain information about a movement of the camera module while successively obtaining the plurality of first images through the camera module, and align the plurality of first images based on the information about the movement of the camera module. 
     In various example embodiments, the at least one processor may be configured to: assign a weight to each of the plurality of aligned first images based on a quality of each of the plurality of aligned first images, and perform an operation of averaging the plurality of aligned first images based on the weight. 
     In various example embodiments, the artificial intelligence model may include a single frame super-resolution (SFSR) artificial intelligence model and a denoising artificial intelligence model. 
       FIG.  6    is a flowchart  600  illustrating an example method for providing an image according to various embodiments. 
     Referring to  FIG.  6   , in operation  601 , in an embodiment, a processor  240  may successively obtain a plurality of first images through a camera module  220 . 
     In operation  603 , in an embodiment, the processor  240  may align the plurality of obtained first images. 
     In an embodiment, the processor  240  may align, based on one image among the plurality of first images, images (e.g., the positions of the images) other than the one image as a reference among the plurality of first images. 
     In an embodiment, the processor  240  may obtain information about a movement of a camera module  220  while successively obtaining a plurality of first images through the camera module  220 . For example, the processor  240  may obtain (e.g., calculate) differences between a position of the camera module  220  at which a first image among the plurality of first images (e.g., an image obtained first among the plurality of first images) is obtained and positions of the camera module  220  at which images after the first image (e.g., images obtained after the first image is obtained) are obtained while successively obtaining the plurality of first images through the camera module  220 . In an embodiment, the processor  240  may obtain the information about the movement of the camera module  220  by comparing pixel values of the first image and pixel values of each of the images after the first image while successively obtaining the plurality of first images through the camera module  220 . 
     In an embodiment, the processor  240  may align the plurality of first images, based on the obtained information about the movement of the camera module  220 . 
     In an embodiment, the processor  240  may shift the positions of the other images, based on the position of the one image as a reference among the plurality of first images, based on the obtained information about the movement of the camera module  220 , thereby aligning the plurality of first images 
     In operation  605 , in an embodiment, the processor  240  may obtain a reference image based on the plurality of aligned first images. 
     In an embodiment, the processor  240  may average the plurality of aligned first images in view of a weight. For example, the processor  240  may assign a weight to each of the plurality of aligned first images, based on the quality of each of the plurality of aligned first images (e.g., the degree to which each of the plurality of aligned first images is blurred and/or the peak signal-to-noise ratio (PSNR) of each of the plurality of aligned first images). Weights assigned to the plurality of aligned first images may be the same or different. However, a method for obtaining the reference image based on the plurality of aligned first images is not limited to the foregoing example. The processor  240  may obtain the reference image using a median of the plurality of aligned first images. 
     In an embodiment, the processor  240  may average the plurality of aligned first images (e.g., pixel values of each of the plurality of aligned first images) in view of the weights assigned to the plurality of aligned first images, thereby obtaining the reference image. 
     In an embodiment, the reference image may be an image in which the strength of an original signal (e.g., a pattern of each of the plurality of aligned first images) is increased in a high-frequency region and the strength of a noise signal is reduced, compared to the plurality of aligned first images. In an embodiment, the reference image may be an image including data of the high-frequency region that is not included in the plurality of aligned first images. In an embodiment, the reference image may be an image including less noise (e.g., a Moiré signal) than the plurality of aligned first images. In an embodiment, the reference image may be an image from which at least part of noise included in the plurality of aligned first images is removed. In an embodiment, the reference image may be an image generated based on at least a portion of each of the plurality of aligned first images. 
     In an embodiment, the processor  240  may average all of the plurality of aligned first images or some images of the plurality of aligned first images in view of the weights. In an embodiment, a weighted average module  340  may transmit the reference image to an artificial intelligence model  231  (e.g., a concatenation module  350 ). 
     In operation  607 , in an embodiment, the processor  240  may obtain a second image (e.g., final image) using the artificial intelligence model  231 , based on the plurality of aligned first images and the reference image. 
     In an embodiment, the processor  240  may convert the plurality of aligned first images and the reference image into one image using the artificial intelligence model  231 . For example, the plurality of aligned first images and the reference image may each have a red channel, a green channel, and a blue channel. The processor  240  may obtain one image having the channels of the plurality of aligned first images and the channels of the reference image from the plurality of aligned first images and the reference image using the artificial intelligence model  231 . 
     In an embodiment, the processor  240  may output, using the artificial intelligence model  231  (e.g., an SFSR module  360 ), an image in which noise (e.g., a Moiré signal) is reduced (or removed) and data of the high-frequency region is restored as result data using the image converted from the plurality of aligned first images and the reference image as input data. For example, the processor  240  may output, using the artificial intelligence model  231  (e.g., an SFSR module  360 ), an image in which each pixel to form the image has a red value, a green value, and a blue value, based on the one image having the channels of the plurality of aligned first images and the channels of the reference image, similarly to the foregoing pixel shifting. 
     In an embodiment, the SFSR module  360  may be an artificial intelligence model using, for example, very deep residual channel attention networks (RCAN). The RCAN may include a convolution layer for extracting a feature (e.g., a shallow feature) of input data, a skip connection (e.g., a long skip connection), a plurality of residual groups, and convolution layers for extracting a feature (e.g., a deep feature) from resulting data of the residual groups. Each residual group of the RCAN may include a plurality of channel attention blocks, a skip connection (e.g., a short skip connection), and a convolution layer, respectively. However, an artificial intelligence network used by the SFSR module  360  is not limited to the RCAN, and various artificial intelligence networks may be used. For example, the SFSR module  360  may use an SRCNN, an FSRCNN, an ESPCN, or a VDSR. 
     In an embodiment, the artificial intelligence model  231  may include a denoising artificial intelligence model (e.g., a denoising CNN model) in addition to or in place of the SFSR module  360 . When the artificial intelligence model  231  uses the denoising artificial intelligence model  231 , noise may be removed from an image, thus obtaining the image having an improved image quality. 
     In an embodiment, the processor  240  may obtain the second image, based on the image output from the artificial intelligence model  231  and the reference image. In an embodiment, the processor  240  may obtain the second image, based on the image output from the artificial intelligence model  231  and the reference image, using the artificial intelligence model  231 . 
     In an embodiment, the second image may be an image in which noise (e.g., a Moire signal) existing in the plurality of first images (e.g., aligned first images) or the reference image is reduced by cancelation and the data in the high-frequency region is amplified. 
     Although  FIG.  6    shows that the processor  240  aligns the plurality of first images and performs a weighted average operation on the plurality of aligned first images without using the artificial intelligence model  231  to thereby obtain the reference image, the disclosure is not limited thereto. For example, the processor  240  may perform an operation of aligning the plurality of first images and/or a weighted average operation on the plurality of aligned first images using the artificial intelligence model  231 . 
       FIG.  7    is a flowchart  700  illustrating an example method for providing an image according to various embodiments. 
     Referring to  FIG.  7   , in operation  701 , in an embodiment, a processor  240  may successively obtain a plurality of first images through a camera module  220 . 
     In operation  703 , in an embodiment, the processor  240  may crop the plurality of first images. In an embodiment, the processor  240  may crop the plurality of first images. In an embodiment, when a zoom input (e.g., a zoom-in input) associated with the camera module  220  is received from a user, the processor  240  may crop the plurality of first images, based on zoom magnification associated with the camera module  220 . For example, when an input to increase the zoom magnification twice from a zoom magnification of 1.0x to a zoom magnification of 2.0x, the processor  240  may crop a ¼area of each image of the plurality of first images, obtained through the entire area of an image sensor of the camera module  220  (or an area of the image sensor corresponding to a currently displayed image), based on the center of the image (or the center of the image being currently displayed through a display  210 ). 
     In operation  705 , in an embodiment, the processor  240  may upscale the plurality of cropped first images. 
     In an embodiment, the processor  240  may upscale the plurality of cropped first images into a plurality of images having a greater size than the size of the plurality of cropped first images. 
     In an embodiment, the processor  240  may perform the operation of upscaling the plurality of cropped first images, based on the zoom magnification associated with the camera module  220 . For example, when the ¼area of each image of the plurality of first images based on the center of the image is cropped based on receiving the input to increase the zoom magnification twice from a zoom magnification of 1.0x to a zoom magnification of 2.0x, the processor  240  may upscale the plurality of cropped first images having a first size into the plurality of scaled first images having a second size four times (e.g., twice in width and twice in length) greater than the first size. 
     In an embodiment, the processor  240  may perform the operation of upscaling the plurality of cropped first images using various algorithms For example, the processor  240  may perform the operation of upscaling the plurality of cropped first images using a nearest neighbor algorithm, a bicubic algorithm, or a bilinear algorithm. However, an algorithm used by the processor  240  to perform the operation of upscaling the plurality of cropped first images is not limited to the foregoing algorithms 
     In an embodiment, the processor  240  may perform an upscaling operation using an artificial intelligence model  231 . 
     In operation  707 , in an embodiment, the processor  240  may align the plurality of upscaled first images. 
     In operation  709 , in an embodiment, the processor  240  may obtain a reference image, based on the plurality of aligned first images. 
     In operation  711 , in an embodiment, a second image (e.g., final image) may be obtained using the artificial intelligence model  231 , based on the plurality of aligned first images and the reference image. 
     Since operation  707  to operation  709  are at least partially the same as or similar to operation  603  to operation  607 , a detailed description thereof may not be repeated. 
       FIG.  8 A  is a diagram illustrating an example method for aligning a plurality of images according to various embodiments. 
       FIG.  8 B  is a diagram illustrating an example method for aligning a plurality of images according to various embodiments. 
     Referring to  FIG.  8 A  and  FIG.  8 B , in an embodiment, reference numeral  801  may denote one image among a plurality of upscaled (e.g., upscaled by a bicubic method) first images. As shown by reference numeral  801 , the one image among the plurality of upscaled first images includes noise  811  (e.g., a Moiré signal) but may not include data of a high-frequency region in some areas. 
     In an embodiment, reference numeral  802  may denote a reference image. Comparing reference numeral  801  and reference numeral  802 , the reference image may have reduced noise strength compared to the upscaled first image and may include the data in the high-frequency region that is not included in the upscaled first image. 
     In an embodiment, reference numeral  803  may denote a final image obtained using an artificial intelligence model  231 , based on a plurality of aligned first images and one image among the plurality of aligned first images in place of the reference image. As shown by reference numeral  803 , the obtained final image includes noise  831 , and the data in the high-frequency region has not been restored in some areas of the obtained image. 
     In an embodiment, reference numeral  804  may denote a second image. In the second image of reference numeral  804  compared with reference numerals  801  to  803 , the strength of noise is significantly reduced and the data of the high-frequency region has been restored. 
     A method for providing an image by an electronic device according to various example embodiments of the disclosure may include: successively obtaining a plurality of first images through a camera module of the electronic device, aligning the plurality of first images, obtaining a reference image based on the plurality of aligned first images, and obtaining a second image using an artificial intelligence model based on the plurality of aligned first images and the reference image. 
     In various example embodiments, the obtaining of the reference image may include obtaining the reference image by performing a weighted average operation on the plurality of aligned first images. 
     In various example embodiments, the second image may have a higher resolution than a resolution of the plurality of first images. 
     In various example embodiments, the method may further include cropping the plurality of first images and upscaling the plurality of cropped first images, and the aligning of the plurality of first images may include aligning the plurality of upscaled first images. 
     In various example embodiments, the cropping of the plurality of first images may include cropping the plurality of first images based on receiving an input to increase the zoom magnification associated with the camera module, and the upscaling of the plurality of cropped first images may include upscaling the plurality of cropped first images, based on the zoom magnification. 
     In various example embodiments, the reference image may include an image in which a strength of an original signal is increased in a high-frequency region and a strength of noise is reduced compared to the plurality of upscaled first images. 
     In various example embodiments, the aligning of the plurality of first images may include: obtaining information about a movement of the camera module while successively obtaining the plurality of first images through the camera module, and aligning the plurality of first images based on the information about the movement of the camera module. 
     In various example embodiments, the obtaining of the reference image may include: assigning a weight to each of the plurality of aligned first images based on a quality of each of the plurality of aligned first images, and performing an operation of averaging the plurality of aligned first images, based on the weight. 
     In various example embodiments, the artificial intelligence model may include an SFSR artificial intelligence model and a denoising artificial intelligence model. 
     In various example embodiments, the obtaining of the reference image may include obtaining the reference image using the artificial intelligence model. 
     The structure of data used in the foregoing embodiments of the disclosure may be recorded in a non-transitory computer-readable recording medium through various methods. The computer-readable recording medium includes a storage medium, such as a magnetic storage medium (e.g., ROM, floppy disk, and hard disk) and an optical reading medium (e.g., CD-ROM and DVD). 
     While the disclosure has been illustrated and described with reference to various example embodiments, it will be understood that the various example embodiments are intended to be illustrative, not limiting. It will be further understood by those skilled in the art that various changes in form and detail may be made without departing from the true spirit and full scope of the disclosure, including the appended claims and their equivalents. It will also be understood that any of the embodiment(s) described herein may be used in conjunction with any other embodiment(s) described herein.