Low-light image enhancement method, system, storage medium and electronic device

Abstract

The present application provides a low-light image enhancement method, system, storage medium and electronic device, the method comprising the following steps: obtaining first enhancement information based on an input image to be enhanced; obtaining second enhancement information and third enhancement information based on input voice information; obtaining fusion information based on the first enhancement information, the second enhancement information and the third enhancement information; obtaining a low-light enhanced image based on the fusion information, the second enhancement information and the third enhancement information. The present application can enhance low-light images to make the images clearer, and at the same time, the present application also uses cross-modal information supplementation to achieve an enhancement effect of local focus points of the image.

Description

Technical Field

[0001] The present application belongs to the technical field of image processing, and in particular, relates to a low-light image enhancement method, system, storage medium and electronic device. Background Art

[0002] Pictures taken under low-light conditions often have unclear contents. Inaccurate recognition of low-light images will cause obstacles to many application scenarios, such as smart transportation, military detection, security monitoring, visual navigation, face detection and other application scenarios. In order to improve the quality and visibility of images under low-light conditions, it is often necessary to enhance low-light images. Currently common methods include histogram equalization, pixel value mapping curves, etc. However, these traditional methods directly operate on image pixels, are prone to exposure, and have slow processing speeds. In addition, the prior art has also proposed a method based on deep learning. This method improves the efficiency of low-light image enhancement, but requires a large amount of data support training in advance. And the above methods are difficult to achieve the enhancement effect of local focus points. Summary of the invention

[0003] The purpose of the present application is to provide a low-light image enhancement method, system, storage medium and electronic device, which can quickly enhance low-light images and simultaneously achieve enhancement of local focus points.

[0004] In a first aspect, the present application provides a low-light image enhancement method, which includes the following steps: obtaining first enhancement information based on an input image to be enhanced; obtaining second enhancement information and third enhancement information based on input voice information; obtaining fusion information based on the first enhancement information, the second enhancement information and the third enhancement information; and obtaining a low-light enhanced image based on the fusion information, the second enhancement information and the third enhancement information.

[0005] In an implementation manner of the first aspect, obtaining first enhancement information based on an input image to be enhanced includes: inputting the image to be enhanced into a VQ-VAE model to obtain the first enhancement information.

[0006] In an implementation of the first aspect, obtaining the second enhanced information and the third enhanced information based on the voice information includes: performing voice recognition on the voice information to obtain text prompt content; performing word segmentation processing on the text prompt content to obtain the second enhanced information; and inputting the voice information into a VQ-VAE model to obtain the third enhanced information.

[0007] In an implementation of the first aspect, obtaining fusion information based on the first enhancement information, the second enhancement information and the third enhancement information includes: performing cross-attention feature fusion on the first enhancement information, the second enhancement information and the third enhancement information to obtain fusion information; wherein the first enhancement information is input information.

[0008] In an implementation of the first aspect, obtaining a low-light enhanced image based on the fusion information, the second enhancement information and the third enhancement information includes: splicing the fusion information, the second enhancement information and the third enhancement information to input into a large language model to obtain a model output result; and obtaining a low-light enhanced image based on the model output result.

[0009] In an implementation of the first aspect, the model output result includes a fusion output result, a second enhancement output result and a third enhancement output result; the fusion output result, the second enhancement output result and the third enhancement output result correspond to the fusion information, the second enhancement information and the third enhancement information respectively.

[0010] In an implementation manner of the first aspect, acquiring a low-light enhanced image based on the model output result includes: inputting the fusion output result into a VQ-VAE model to acquire the low-light enhanced image.

[0011] In a second aspect, the present application provides a low-light image enhancement system, the system comprising an image information acquisition module, used to acquire first enhancement information based on an input image to be enhanced; a voice information acquisition module, used to acquire second enhancement information and third enhancement information based on input voice information; a fusion module, used to acquire fusion information based on the first enhancement information, the second enhancement information and the third enhancement information; and an enhancement module, used to acquire a low-light enhanced image based on the fusion information, the second enhancement information and the third enhancement information.

[0012] In a third aspect, the present application provides an electronic device, the electronic device comprising: a processor and a memory;

[0013] The memory is used to store computer programs;

[0014] The processor is used to execute the computer program stored in the memory so that the electronic device performs the above-mentioned low-light image enhancement method.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned low-light image enhancement method when executed by an electronic device.

[0016] As described above, the low-light image enhancement method, system, storage medium and electronic device described in the present application have the following beneficial effects: the present application can quickly enhance low-light images, and can add voice information and introduce cross-modal information during enhancement to achieve an enhancement effect on local focus points of the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a schematic diagram of a scene of an electronic device in an embodiment of the present application.

[0018] Figure 2 Shown is a flow chart of a low-light image enhancement method according to an embodiment of the present application.

[0019] Figure 3 Shown is a flow chart of a low-light image enhancement method according to an embodiment of the present application.

[0020] Figure 4 Shown is a flow chart of a low-light image enhancement method according to an embodiment of the present application.

[0021] Figure 5 Shown is a schematic structural diagram of a low-light image enhancement system according to an embodiment of the present application.

[0022] Figure 6 Shown is a schematic structural diagram of an electronic device of the present application in one embodiment. DETAILED DESCRIPTION

[0023] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0024] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0025] The following embodiments of the present application provide a low-light image enhancement method, which can be applied to Figure 1The electronic device shown. The electronic device described in this application may include a mobile phone 11 with wireless charging function, a tablet computer 12, a laptop computer 13, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc. The embodiment of this application does not impose any restrictions on the specific type of the electronic device.

[0026] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next generation communication system, such as a mobile terminal in a 5G network, a mobile terminal in a future evolved Public Land Mobile Network (PLMN), or a mobile terminal in a future evolved Non-terrestrial Network (NTN).

[0027] For example, the electronic device can communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobilecommunication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS can include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS) and / or Satellite Based Augmentation Systems (SBAS).

[0028] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0029] like Figure 2 As shown, in one embodiment, the low-light image enhancement method of the present application includes steps S1 to S4.

[0030] S1: Acquire first enhancement information based on an input image to be enhanced.

[0031] Specifically, the image to be enhanced is input into the VQ-VAE model to obtain the first enhancement information. The VQ-VAE model discretizes the reference text image to obtain a quantization vector. The VQ-VAE model is a powerful unsupervised representation learning model, which uses a codebook mechanism to encode images into discrete vectors and has strong representation capabilities. Step S1 inputs the image to be enhanced into the encoder module of the VQ-VAE model, and tokenizes the image to be enhanced through the encoder of the VQ-VAE model to obtain the first enhancement information.

[0032] S2: Acquire second enhanced information and third enhanced information based on the input voice information.

[0033] like Figure 3 As shown, step S2 includes steps S21 and S22.

[0034] S21: Acquire second enhanced information based on the input voice information.

[0035] Specifically, the implementation of acquiring the second enhanced information includes: performing speech recognition on the speech information to acquire text prompt content; performing word segmentation processing on the text prompt content to acquire the second enhanced information.

[0036] In some embodiments, the content in the voice information is recognized by the ASR module as text prompt content. The ASR module, also known as the speech recognition module, processes the voice information after it is input into the ASR module, splits it by frame (millisecond level), and converts the split small waveforms into multi-dimensional vector information according to the characteristics of the human ear. After that, the information is recognized as a state through the acoustic model, and then the information is combined to form phonemes, and finally the phonemes are combined into words and connected into sentences. For example, if you want to enhance books in low-light images, the voice content is "books", and the ASR module recognizes "books" as text prompt content.

[0037] In some embodiments, the text prompt content is segmented by a Tokenizer segmentation algorithm to obtain the second enhanced information. The Tokenizer segmentation algorithm is the most basic component of the NLP large model. Based on the Tokenizer, the text can be converted into an independent token list, and then converted into an input vector to become an input form that the computer can understand. Among them, according to different segmentation granularities, the tokenizer can be divided into: word-based segmentation, character-based segmentation and subword-based segmentation. Subword-based segmentation is the current mainstream segmentation method, mainly including three segmentation models: BPE, WordPiece and Unigram. In the above embodiment, "books" can be converted into the second enhanced information through the BPE segmentation model.

[0038] S22: Acquire third enhanced information based on the input voice information.

[0039] Specifically, the implementation method of obtaining the third enhanced information includes: inputting the speech information into the VQ-VAE model to obtain the third enhanced information. In some embodiments, the third enhanced information is obtained based on the original speech information extraction through the encoder module of the VQ-VAE model.

[0040] It should be noted that there is no order between steps S21 and S22, and step S2 is only described here as an example.

[0041] S3: Acquire fusion information based on the first enhancement information, the second enhancement information and the third enhancement information.

[0042] Specifically, cross-attention feature fusion is performed on the first enhanced information, the second enhanced information and the third enhanced information to obtain fused information; wherein the first enhanced information is input information.

[0043] In some embodiments, the first enhanced information is taken as Q, the second enhanced information is taken as K, and the second enhanced information is taken as V. The cross-attention feature fusion is performed through the cross-attention mechanism to obtain the fused information. Among them, Query represents the input information, which has a guiding role; Key represents the content information, which represents other content to be matched; Value represents the information itself, which is just the information that simply expresses the input feature.

[0044] It should be noted that, since the present application is to enhance low-light images and the final output is an image, Q must be the first enhancement information.

[0045] S4: Acquire a low-light enhanced image based on the fusion information, the second enhancement information and the third enhancement information.

[0046] like Figure 4 As shown, step S4 includes steps S41 and S42.

[0047] S41: splicing the fusion information, the second enhanced information and the third enhanced information to input into a large language model to obtain a model output result.

[0048] Specifically, large language models (LLMs) are artificial intelligence models designed to understand and generate human language. They can perform a wide range of tasks, including text summarization, translation, sentiment analysis, and more, by training on large amounts of text data. These models are often based on deep learning architectures such as transformers, which enable them to demonstrate impressive capabilities on a variety of natural language processing tasks. For example, GPT-4 is a multimodal pre-trained large model that can receive image and text inputs, and users can specify any visual or language task to make GPT-4 generate text output, including natural language, code, etc.

[0049] In some embodiments, the fusion information, the second enhancement information and the third enhancement information are spliced ​​and input into the GPT-4 model, and GPT-4 will output output results corresponding to the fusion information, the second enhancement information and the third enhancement information respectively.

[0050] Specifically, the model output result includes a fusion output result, a second enhancement output result and a third enhancement output result; the fusion output result, the second enhancement output result and the third enhancement output result correspond to the fusion information, the second enhancement information and the third enhancement information respectively.

[0051] S42: Acquire a low-light enhanced image based on the model output result.

[0052] Specifically, the fusion output result is selected to be input into the VQ-VAE model to obtain the low-light enhanced image.

[0053] In some embodiments, the decoder of the VQ-VAE model converts the fusion output result into an image pixel space, that is, a low-light enhanced image for output. The low-light enhanced image obtained by this application will become clear, and the object content in the voice information will be particularly enhanced. For example, in the above embodiments, the object content of the voice information is "books", then in these embodiments, the enhancement effect of the "book" object in the obtained low-light enhanced image will be more obvious, achieving the purpose of local enhancement.

[0054] The protection scope of the low-light image enhancement method described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0055] The embodiments of the present application also provide a low-light image enhancement system, which can implement the low-light image enhancement method described in the present application. However, the implementation device of the low-light image enhancement system described in the present application includes but is not limited to the structure of the low-light image enhancement system listed in the embodiments. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0056] like Figure 5 As shown, in one embodiment, the low-light image enhancement system of the present application includes an image information acquisition module 41 , a voice information acquisition module 42 , a fusion module 43 and an enhancement module 44 .

[0057] An image information acquisition module 41 is used to acquire first enhancement information based on an input image to be enhanced;

[0058] A voice information acquisition module 42, used to acquire second enhanced information and third enhanced information based on input voice information;

[0059] A fusion module 43, configured to obtain fusion information based on the first enhancement information, the second enhancement information and the third enhancement information;

[0060] The enhancement module 44 is configured to obtain a low-light enhanced image based on the fusion information, the second enhancement information and the third enhancement information.

[0061] Among them, the structures and principles of the image information acquisition module 41, the voice information acquisition module 42, the fusion module 43 and the enhancement module 44 correspond one to one with the steps in the above-mentioned low-light image enhancement method, so they are not repeated here.

[0062] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0063] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0064] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0065] The present application embodiment also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by a program to instruct the processor, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state hard disk (SSD)), etc.

[0066] The embodiment of the present application further provides an electronic device, wherein the electronic device includes a processor and a memory.

[0067] The memory is used to store computer programs.

[0068] The memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[0069] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device performs the above-mentioned low-light image enhancement method.

[0070] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0071] like Figure 6As shown, the electronic device of the present application is in the form of a general computing device. The components of the electronic device may include but are not limited to: one or more processors or processing units 51, a memory 52, and a bus 53 connecting different system components (including the memory 52 and the processing unit 51).

[0072] Bus 53 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0073] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0074] The memory 52 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 521 and / or cache memory 522. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 523 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 53 via one or more data medium interfaces. The memory 52 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0075] A program / utility 524 having a set (at least one) of program modules 5241 may be stored, for example, in the memory 52, such program modules 5241 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 5241 generally perform the functions and / or methods of the embodiments described herein.

[0076] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 54. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 55. Figure 6 As shown, the network adapter 55 communicates with other modules of the electronic device via the bus 53. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0077] The present application embodiment may also provide a computer program product, the computer program product including one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer or data center.

[0078] When the computer program product is executed by a computer, the computer executes the method described in the above method embodiment. The computer program product may be a software installation package, and when the above method is required, the computer program product may be downloaded and executed on a computer.

[0079] The present application provides a low-light image enhancement method, system, storage medium and electronic device, which can enhance low-light images to make the images clearer. At the same time, the present application also uses cross-modal information supplementation to achieve the enhancement effect of local focus points of the image.

[0080] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0081] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A low-light image enhancement method, characterized in that: include: Acquire first enhancement information based on the input image to be enhanced; acquiring second enhanced information and third enhanced information based on the input voice information; The speech information includes a local focus point in the image to be enhanced; Acquire fusion information based on the first enhancement information, the second enhancement information, and the third enhancement information; Enhance the local focus point based on the fusion information, the second enhancement information and the third enhancement information to acquire a low-light enhanced image; Wherein, acquiring the first enhancement information based on the input image to be enhanced includes: inputting the image to be enhanced into a VQ-VAE model to acquire the first enhancement information; Acquiring the second enhanced information and the third enhanced information based on the voice information includes: performing voice recognition on the voice information to acquire text prompt content; performing word segmentation processing on the text prompt content to acquire the second enhanced information; and The speech information is input into the VQ-VAE model to obtain the third enhanced information.

2. The low-light image enhancement method according to claim 1, characterized in that: Acquiring fusion information based on the first enhanced information, the second enhanced information, and the third enhanced information includes: Cross-attention feature fusion is performed on the first enhanced information, the second enhanced information and the third enhanced information to obtain fused information; wherein the first enhanced information is input information.

3. The low-light image enhancement method according to claim 1, characterized in that: Acquiring a low-light enhanced image based on the fusion information, the second enhancement information, and the third enhancement information includes: splicing the fused information, the second enhanced information and the third enhanced information to input into a large language model to obtain a model output result; A low-light enhanced image is acquired based on the model output result.

4. The low-light image enhancement method according to claim 3, characterized in that: The model output result includes a fusion output result, a second enhanced output result and a third enhanced output result; The fused output result, the second enhanced output result and the third enhanced output result correspond to the fused information, the second enhanced information and the third enhanced information respectively.

5. The low-light image enhancement method according to claim 4, characterized in that: Acquiring a low-light enhanced image based on the output result of the model includes: The fused output result is input into the VQ-VAE model to obtain the low-light enhanced image.

6. A low-light image enhancement system, characterized in that: include: An image information acquisition module, used for acquiring first enhancement information based on an input image to be enhanced; A voice information acquisition module, used to acquire the second enhanced information and the third enhanced information based on the input voice information; The speech information includes a local focus point in the image to be enhanced; a fusion module, configured to obtain fusion information based on the first enhancement information, the second enhancement information and the third enhancement information; an enhancement module, configured to enhance the local focus point based on the fusion information, the second enhancement information and the third enhancement information to obtain a low-light enhanced image; Wherein, acquiring the first enhancement information based on the input image to be enhanced includes: inputting the image to be enhanced into a VQ-VAE model to acquire the first enhancement information; Acquiring the second enhanced information and the third enhanced information based on the voice information includes: performing voice recognition on the voice information to acquire text prompt content; performing word segmentation processing on the text prompt content to acquire the second enhanced information; and The speech information is input into the VQ-VAE model to obtain the third enhanced information.

7. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory, so that the electronic device executes the low-light image enhancement method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the low-light image enhancement method according to any one of claims 1 to 5 is implemented.

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