Exposure image enhancement method and system

Abstract

The invention provides an exposure image enhancement method and system, and the method comprises the steps: obtaining a to-be-processed exposure image, and carrying out the preprocessing of the to-be-processed exposure image, so as to obtain a processed image; performing Laplace reconstruction processing on the processed image to obtain a reconstructed image; performing artifact removal filtering processing on the reconstructed image to obtain a filtered image; performing enhanced fusion processing on the filtered image to obtain a fused image; according to the method, the image can be preliminarily enhanced, edge details of the image can be fully embodied, meanwhile, part of noise in the image can be denoised, meanwhile, noise such as ghosting, artifacts and halo in the image can be fully filtered out, the visual effect of the image is improved, and the image quality is improved. Moreover, the image can be further enhanced, the image is prevented from being distorted, the definition of the image is greatly improved, and the detail information of the image is fully restored.

Description

Technical Field

[0001] The present invention belongs to the technical field of image enhancement, and in particular relates to an exposure image enhancement method and system. Background Art

[0002] Existing ordinary camera equipment usually captures images through a single shot. The dynamic range covered in the image is much smaller than the dynamic range of human perception of nature, so there is often a big difference between the captured image and the real scene seen by the human eye. The dynamic range of an image refers to the ratio of the maximum and minimum light intensity in the scene. The larger the dynamic range, the stronger the ability to restore the information in the real scene. Especially for scenes with a large dynamic range in nature, the captured images often have local underexposure or overexposure, which greatly affects the appearance of the image.

[0003] Therefore, in order to improve the clarity of the exposed image, it is usually necessary to perform a certain degree of enhancement processing on the exposed image to improve the clarity of the exposed image and fully obtain the information in the image. However, for the existing enhancement methods, it is usually only enhanced through filtering means, such as median filtering, Gaussian filtering process, etc. The above filtering process has insufficient processing capabilities for the exposed image and cannot effectively improve the image visual effect. At the same time, it is also impossible to fully remove artifacts, halos and other noises in the image. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an exposure image enhancement method and system for solving the technical problems in the prior art.

[0005] On the one hand, the present invention provides the following technical solution, a method for enhancing an exposure image, comprising: Acquire an exposure image to be processed, and preprocess the exposure image to be processed to obtain a processed image; Performing Laplace reconstruction processing on the processed image to obtain a reconstructed image; Performing a de-artifacting filtering process on the reconstructed image to obtain a filtered image; Performing enhanced fusion processing on the filtered image to obtain a fused image; The fused image is subjected to transmission enhancement processing to obtain an enhanced image.

[0006] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first obtains the exposure image to be processed, and pre-processes the exposure image to be processed to obtain a processed image; then performs Laplace reconstruction processing on the processed image to obtain a reconstructed image; then performs de-artifacting filtering processing on the reconstructed image to obtain a filtered image; then performs enhancement fusion processing on the filtered image to obtain a fused image; finally, performs transmission enhancement processing on the fused image to obtain an enhanced image. The present invention can perform preliminary enhancement on the image by reconstructing the image, fully reflecting the edge details of the image while also being able to denoise part of the noise in the image. Then, the image is de-artifacting filtered to fully filter out the ghosting, artifacts, halo and other noises in the image, thereby improving the visual effect of the image. Finally, the image is enhanced, fused and transmission enhanced to further enhance the image, thereby avoiding image distortion, greatly improving the clarity of the image, and fully restoring the detailed information of the image.

[0007] Preferably, the step of preprocessing the exposure image to be processed to obtain a processed image includes: The exposure image to be processed is subjected to size cropping, image rotation, image filtering, and image denoising in sequence to obtain a processed image.

[0008] Preferably, the step of performing Laplace reconstruction processing on the processed image to obtain a reconstructed image includes: Construct a Gaussian pyramid, input the processed image into the Gaussian pyramid, and Down-sampling the processed image of the layer to obtain a sampled image; Through the Gaussian pyramid The layer is subjected to Gaussian filtering to obtain a Gaussian filtered image; The Gaussian pyramid image is sequentially downsampled and Gaussian filtered to obtain An image of a pyramid; For The pyramid image is upsampled to obtain an upsampled image. The upsampled image and The Laplace difference operation is performed on the pyramid image to obtain Reconstructed image.

[0009] Preferably, the step of performing artifact removal filtering on the reconstructed image to obtain a filtered image comprises: Set the reconstructed image The filter window centered at the pixel point is calculated Edge filter factor of pixel point : ; In the formula, Represents the number of pixels in the reconstructed image. Indicates The variance within the filter window centered at the pixel, Indicates The variance within the filter window centered at the pixel, represents the regulation constant; Pre-set adjustment factors , based on the edge filter factor The regulatory factor Perform update processing to obtain the update adjustment factor : ; In the formula, Indicates The filter window is centered on the pixel point. , Respectively Linear coefficients and linear constants within express The pixel value of the pixel; Based on the update adjustment factor Calculate filter weights : ; In the formula, Indicates The number of pixels in the filter window centered at the pixel point, express The coordinates of the pixel point, express The pixel value of the pixel, Indicates The pixel mean of the filter window centered at the pixel point; Based on the filter weight The reconstructed image is filtered to obtain a filtered image. : ; In the formula, Indicates that the coordinates in the reconstructed image are Pixels.

[0010] Preferably, the step of performing enhanced fusion processing on the filtered image to obtain a fused image comprises: Establish the weight matrix equation: ; In the formula, is a standard matrix, Indicates element chunking, represents the Toeplitz matrix, represents the diagonal matrix construction operation, Indicates the weight matrix row vectors, represents the discrete gradient operator, represents the filtered image, represents the balance coefficient; Based on the weight matrix, the row vector in the weight matrix is ​​solved , and according to the row vector in the weight matrix Determine the weight matrix ; Identify the initial exposure rate of the filtered image , for the initial exposure rate Adjust the exposure ratio : ; In the formula, represents the image entropy calculation, Represents the shared parameters of the filtered image in the RGB three channels, Represents the mean of the three channels of RGB for the filtered image; Based on the weight matrix Adjust the exposure as described The filtered images are fused to obtain a fused image. : ; In the formula, Indicates the number of filtered images.

[0011] Preferably, the step of performing transmission enhancement processing on the fused image to obtain an enhanced image comprises: Calculate the fused image The initial transmittance : ; In the formula, represents the correction factor, represents the local area in the fused image, Represents the image of one of the RGB channels of the fused image. Indicates the atmospheric light value of one of the RGB channels; The initial transmittance Perform update processing to obtain updated transmittance : ; In the formula, Respectively represent the image of one of the RGB channels of the fused image; Based on the updated transmittance The fused image Perform guided filtering to obtain guided transmittance : ; In the formula, represents the guided filter function, represents the guided filter window, represents the regularization factor; Based on the guided transmittance The fused image Perform enhancement processing to obtain an enhanced image : ; In the formula, Represents the intensity of light in the fused image.

[0012] In a second aspect, the present invention provides the following technical solution, an exposure image enhancement system, the system comprising: A preprocessing module is used to obtain an exposure image to be processed and preprocess the exposure image to be processed to obtain a processed image; A reconstruction module, used for performing Laplace reconstruction processing on the processed image to obtain a reconstructed image; A filtering module, used for performing a de-artifacting filtering process on the reconstructed image to obtain a filtered image; A fusion module, used for performing enhanced fusion processing on the filtered image to obtain a fused image; The enhancement module is used to perform transmission enhancement processing on the fused image to obtain an enhanced image.

[0013] Preferably, the reconstruction module includes: The down sampling submodule is used to construct a Gaussian pyramid, input the processed image into the Gaussian pyramid, and perform down sampling on the Gaussian pyramid. Down-sampling the processed image of the layer to obtain a sampled image; Gaussian submodule, used to pass the Gaussian pyramid The layer is subjected to Gaussian filtering to obtain a Gaussian filtered image; The repeating submodule is used to sequentially perform downsampling and Gaussian filtering on the Gaussian pyramid image to obtain An image of a pyramid; The upsampling submodule is used to The pyramid image is upsampled to obtain an upsampled image. The upsampled image and The Laplace difference operation is performed on the pyramid image to obtain Reconstructed image.

[0014] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the exposure image enhancement method as described above when executing the computer program.

[0015] In a fourth aspect, the present invention provides the following technical solution: a storage medium having a computer program stored thereon, wherein the computer program implements the exposure image enhancement method as described above when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of an exposure image enhancement method provided in Embodiment 1 of the present invention; Figure 2 A structural block diagram of an exposure image enhancement system provided in Embodiment 2 of the present invention; Figure 3 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0020] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0021] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0022] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0023] Embodiment 1 In the first embodiment of the present invention, Figure 1 As shown, an exposure image enhancement method includes: S1, obtaining an exposure image to be processed, and preprocessing the exposure image to be processed to obtain a processed image; Specifically, in order to improve the speed and accuracy of subsequent image processing, the image needs to be preprocessed first to preliminarily remove the noise in the image; Step S1 specifically includes: performing size cropping, image rotation, image filtering, and image denoising processing on the exposure image to be processed in sequence to obtain a processed image.

[0024] S2, performing Laplace reconstruction processing on the processed image to obtain a reconstructed image; Wherein, the step S2 comprises: S21, constructing a Gaussian pyramid, inputting the processed image into the Gaussian pyramid, Down-sampling the processed image of the layer to obtain a sampled image; Specifically, the Gaussian pyramid is a multi-layered tower structure, and there is an input-output relationship between each layer, that is, the image output by the upper layer is input to the lower layer for processing.

[0025] S22, through the Gaussian pyramid The layer is subjected to Gaussian filtering to obtain a Gaussian filtered image.

[0026] S23, performing downsampling processing and Gaussian filtering processing on the Gaussian pyramid image in sequence to obtain An image of a pyramid; Specifically, for all pyramid images, the processed image is first input to the bottom layer of the Gaussian pyramid, and then processed by downsampling and Gaussian filtering in several intermediate layers, and finally the corresponding image is output at the top layer. The bottom layer is the initial processed image with the highest resolution, and the top layer is the image with the lowest resolution.

[0027] S24, for The pyramid image is upsampled to obtain an upsampled image. The upsampled image and The Laplace difference operation is performed on the pyramid image to obtain Zhang reconstructed image; Specifically, the resolution of the image can be slowly restored through upsampling processing and differential processing to fully reflect the details in the image.

[0028] S3, performing a de-artifacting filtering process on the reconstructed image to obtain a filtered image; Wherein, the step S3 comprises: S31, setting the reconstructed image The filter window centered at the pixel point is calculated Edge filter factor of pixel point : ; In the formula, Represents the number of pixels in the reconstructed image. Indicates The variance within the filter window centered at the pixel, Indicates The variance within the filter window centered at the pixel, represents the regulation constant; Specifically, the filtering process is guided filtering, and the adjustment constant is a value greater than 0, in order to ensure that the values ​​of the numerator and denominator in the above formula are positive numbers.

[0029] S32. Pre-set adjustment factor , based on the edge filter factor The regulatory factor Perform update processing to obtain the update adjustment factor : ; In the formula, Indicates The filter window is centered on the pixel point. , Respectively Linear coefficients and linear constants within express The pixel value of the pixel; Specifically, updating the adjustment factor can fully consider the differences between pixels, thereby ensuring that areas in the image where the grayscale value changes greatly avoid noise such as halos and artifacts.

[0030] S33, based on the update adjustment factor Calculate filter weights : ; In the formula, Indicates The number of pixels in the filter window centered at the pixel point, express The coordinates of the pixel point, express The pixel value of the pixel, Indicates The pixel mean of the filter window centered at the pixel point; Specifically, when the filter window remains unchanged, the larger the update adjustment factor, the smoother the filtered image, and the size of the filter window determines the filtering ability. The larger the filter window, the more pixels participate in the filtering, and the smoother the filtered image is and the stronger the noise removal ability is.

[0031] S34, based on the filtering weight The reconstructed image is filtered to obtain a filtered image. : ; In the formula, Indicates that the coordinates in the reconstructed image are Pixels of Specifically, a weighted fusion process is performed on the pixels within the filter window in each reconstructed image to obtain a filtered image.

[0032] S4, performing enhancement fusion processing on the filtered image to obtain a fused image; Wherein, the step S4 comprises: S41. Establish the weight matrix equation: ; In the formula, is a standard matrix, Indicates element chunking, represents the Toeplitz matrix, represents the diagonal matrix construction operation, Indicates the weight matrix row vectors, represents the discrete gradient operator, represents the filtered image, represents the balance coefficient; Specifically, the balance coefficient is used to balance the gap between the Toeplitz matrix and the standard matrix and improve the effectiveness of the weight matrix equation.

[0033] S42, solving the row vector in the weight matrix based on the weight matrix , and according to the row vector in the weight matrix Determine the weight matrix ; Specifically, after all row vectors in the weight matrix are determined, all row vectors are combined vertically to obtain the weight matrix.

[0034] S43, identifying the initial exposure rate of the filtered image , for the initial exposure rate Adjust the exposure ratio : ; In the formula, represents the image entropy calculation, Represents the shared parameters of the filtered image in the RGB three channels, Represents the mean of the three channels of RGB for the filtered image; Specifically, the shared parameters here Specifically, it is the ratio of the color components of the three channels of the image after exposure to that before exposure.

[0035] S44, based on the weight matrix Adjust the exposure as described The filtered images are fused to obtain a fused image. : ; In the formula, Indicates the number of filtered images.

[0036] S5, performing transmission enhancement processing on the fused image to obtain an enhanced image; Wherein, the step S5 comprises: S51, calculating the fused image The initial transmittance : ; In the formula, represents the correction factor, represents the local area in the fused image, Represents the image of one of the RGB channels of the fused image. Indicates the atmospheric light value of one of the RGB channels; S52, the initial transmittance Perform update processing to obtain updated transmittance : ; In the formula, Respectively represent the image of one of the RGB channels of the fused image; Specifically, the purpose of the update here is to correct the parts with large differences in pixel channel values, fine-tune the parts with small differences, and control the transmittance between 0-1.

[0037] S53, based on the updated transmittance The fused image Perform guided filtering to obtain guided transmittance : ; In the formula, represents the guided filter function, represents the guided filter window, represents the regularization factor.

[0038] S54, based on the guided transmittance The fused image Perform enhancement processing to obtain an enhanced image : ; In the formula, Represents the intensity of light in the fused image.

[0039] The exposure image enhancement method provided in the first embodiment of the present invention first obtains the exposure image to be processed, and pre-processes the exposure image to be processed to obtain a processed image; then performs Laplace reconstruction processing on the processed image to obtain a reconstructed image; then performs de-artifact filtering processing on the reconstructed image to obtain a filtered image; then performs enhancement fusion processing on the filtered image to obtain a fused image; finally, performs transmission enhancement processing on the fused image to obtain an enhanced image. The present invention can perform preliminary enhancement on the image by reconstructing the image, fully reflecting the edge details of the image while also being able to denoise part of the noise in the image. Then, the image is subjected to de-artifact filtering, which can fully filter out the noise such as ghosting, artifacts, halo, etc. in the image, thereby improving the visual effect of the image. Finally, the image is enhanced, fused and transmission enhanced, which can further enhance the image, avoid image distortion, greatly improve the clarity of the image, and fully restore the detail information of the image.

[0040] Embodiment 2 like Figure 2 As shown, in the second embodiment of the present invention, an exposure image enhancement system is provided, and the system includes: A preprocessing module 1 is used to obtain an exposure image to be processed and preprocess the exposure image to be processed to obtain a processed image; A reconstruction module 2, used for performing Laplace reconstruction processing on the processed image to obtain a reconstructed image; A filtering module 3, used for performing a de-artifacting filtering process on the reconstructed image to obtain a filtered image; A fusion module 4 is used to perform enhanced fusion processing on the filtered image to obtain a fused image; The enhancement module 5 is used to perform transmission enhancement processing on the fused image to obtain an enhanced image.

[0041] Specifically, the reconstruction module 2 includes: The down sampling submodule is used to construct a Gaussian pyramid, input the processed image into the Gaussian pyramid, and perform down sampling on the Gaussian pyramid. Down-sampling the processed image of the layer to obtain a sampled image; Gaussian submodule, used to pass the Gaussian pyramid The layer is subjected to Gaussian filtering to obtain a Gaussian filtered image; The repeating submodule is used to sequentially perform downsampling and Gaussian filtering on the Gaussian pyramid image to obtain An image of a pyramid; The upsampling submodule is used to The pyramid image is upsampled to obtain an upsampled image. The upsampled image and The Laplace difference operation is performed on the pyramid image to obtain Reconstructed image.

[0042] The filtering module 3 comprises: The window determination submodule is used to set the window on the reconstructed image. The filter window centered at the pixel point is calculated Edge filter factor of pixel point : ; In the formula, Represents the number of pixels in the reconstructed image. Indicates The variance within the filter window centered at the pixel, Indicates The variance within the filter window centered at the pixel, represents the regulation constant; Factor update submodule, used to pre-set adjustment factors , based on the edge filter factor The regulatory factor Perform update processing to obtain the update adjustment factor : ; In the formula, Indicates The filter window is centered on the pixel point. , Respectively Linear coefficients and linear constants within express The pixel value of the pixel; A filter weight submodule is used to update the adjustment factor based on the Calculate filter weights : ; In the formula, Indicates The number of pixels in the filter window centered at the pixel point, express The coordinates of the pixel point, express The pixel value of the pixel, Indicates The pixel mean of the filter window centered at the pixel point; An image filtering submodule is used to filter the image based on the filter weights. The reconstructed image is filtered to obtain a filtered image. : ; In the formula, Indicates that the coordinates in the reconstructed image are Pixels.

[0043] The fusion module 4 includes: The equation submodule is used to establish the weight matrix equation: ; In the formula, is a standard matrix, Indicates element chunking, represents the Toeplitz matrix, represents the diagonal matrix construction operation, Indicates the weight matrix row vectors, represents the discrete gradient operator, represents the filtered image, represents the balance coefficient; A matrix determination submodule is used to solve the row vectors in the weight matrix based on the weight matrix. , and according to the row vector in the weight matrix Determine the weight matrix ; An adjustment submodule for identifying an initial exposure rate of the filtered image , for the initial exposure rate Adjust the exposure ratio : ; In the formula, represents the image entropy calculation, Represents the shared parameters of the filtered image in the RGB three channels, Represents the mean of the three channels of RGB for the filtered image; A fusion submodule is used to Adjust the exposure as described The filtered images are fused to obtain a fused image. : ; In the formula, Indicates the number of filtered images.

[0044] The enhancement module 5 comprises: The transmittance submodule is used to calculate the fused image The initial transmittance : ; In the formula, represents the correction factor, represents the local area in the fused image, Represents the image of one of the RGB channels of the fused image. Indicates the atmospheric light value of one of the RGB channels; The transmittance updating submodule is used to update the initial transmittance Perform update processing to obtain updated transmittance : ; In the formula, Respectively represent the image of one of the RGB channels of the fused image; A guided filtering submodule is used to update the transmittance based on the The fused image Perform guided filtering to obtain guided transmittance : ; In the formula, represents the guided filter function, represents the guided filter window, represents the regularization factor; The image enhancement submodule is used to guide the transmittance based on the The fused image Perform enhancement processing to obtain an enhanced image : ; In the formula, Represents the intensity of light in the fused image.

[0045] In some other embodiments of the present invention, the embodiments of the present invention provide the following technical solution: a computer, comprising a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101, wherein the processor 101 implements the exposure image enhancement method as described above when executing the computer program.

[0046] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0047] Among them, the memory 102 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 102 may be inside or outside the data processing device. In a specific embodiment, the memory 102 is a non-volatile memory. In a specific embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0048] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0049] The processor 101 implements the above-mentioned exposure image enhancement method by reading and executing the computer program instructions stored in the memory 102 .

[0050] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 3 As shown, the processor 101, the memory 102, and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0051] The communication interface 103 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present invention. The communication interface 103 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0052] The bus 100 includes hardware, software or both, and couples the components of the computer device to each other. The bus 100 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 100 may include one or more buses. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0053] The computer can execute the exposure image enhancement method of the present invention based on the exposure image enhancement system acquired, thereby realizing exposure image enhancement.

[0054] In some further embodiments of the present invention, in combination with the above-mentioned exposure image enhancement method, the embodiments of the present invention provide the following technical solution: a storage medium having a computer program stored thereon, and the above-mentioned exposure image enhancement method is implemented when the computer program is executed by a processor.

[0055] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0056] More specific examples of readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0057] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0058] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0059] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for enhancing an exposure image, characterized in that: include: Acquire an exposure image to be processed, and preprocess the exposure image to be processed to obtain a processed image; Performing Laplace reconstruction processing on the processed image to obtain a reconstructed image; Performing a de-artifacting filtering process on the reconstructed image to obtain a filtered image; Performing enhanced fusion processing on the filtered image to obtain a fused image; The fused image is subjected to transmission enhancement processing to obtain an enhanced image.

2. The exposure image enhancement method according to claim 1, characterized in that: The step of preprocessing the exposure image to be processed to obtain a processed image comprises: The exposure image to be processed is subjected to size cropping, image rotation, image filtering, and image denoising in sequence to obtain a processed image.

3. The exposure image enhancement method according to claim 1, characterized in that: The step of performing Laplace reconstruction processing on the processed image to obtain a reconstructed image comprises: Construct a Gaussian pyramid, input the processed image into the Gaussian pyramid, and Down-sampling the processed image of the layer to obtain a sampled image; Through the Gaussian pyramid The layer is subjected to Gaussian filtering to obtain a Gaussian filtered image; The Gaussian pyramid image is sequentially downsampled and Gaussian filtered to obtain An image of a pyramid; For The pyramid image is upsampled to obtain an upsampled image. The upsampled image and The Laplace difference operation is performed on the pyramid image to obtain Reconstructed image.

4. The exposure image enhancement method according to claim 1, characterized in that: The step of performing artifact removal filtering on the reconstructed image to obtain a filtered image comprises: Set the reconstructed image The filter window centered at the pixel point is calculated Edge filter factor of pixel point : ； In the formula, Represents the number of pixels in the reconstructed image. Indicates The variance within the filter window centered at the pixel, Indicates The variance within the filter window centered at the pixel, represents the regulation constant; Pre-set adjustment factors , based on the edge filter factor The regulatory factor Perform update processing to obtain the update adjustment factor : ； In the formula, Indicates The filter window is centered on the pixel point. , Respectively Linear coefficients and linear constants within express The pixel value of the pixel; Based on the update adjustment factor Calculate filter weights : ； In the formula, Indicates The number of pixels in the filter window centered at the pixel point, express The coordinates of the pixel point, express The pixel value of the pixel, Indicates The pixel mean of the filter window centered at the pixel point; Based on the filter weight The reconstructed image is filtered to obtain a filtered image. : ； In the formula, Indicates that the coordinates in the reconstructed image are Pixels.

5. The exposure image enhancement method according to claim 1, characterized in that: The step of performing enhanced fusion processing on the filtered image to obtain a fused image comprises: Establish the weight matrix equation: ； In the formula, is a standard matrix, Indicates element chunking, represents the Toeplitz matrix, represents the diagonal matrix construction operation, Indicates the weight matrix row vectors, represents the discrete gradient operator, represents the filtered image, represents the balance coefficient; Based on the weight matrix, the row vector in the weight matrix is ​​solved , and according to the row vector in the weight matrix Determine the weight matrix ; Identify the initial exposure rate of the filtered image , for the initial exposure rate Adjust the exposure ratio : ； In the formula, represents the image entropy calculation, Represents the shared parameters of the filtered image in the RGB three channels, Represents the mean of the three channels of RGB for the filtered image; Based on the weight matrix Adjust the exposure as described The filtered images are fused to obtain a fused image. : ； In the formula, Indicates the number of filtered images.

6. The exposure image enhancement method according to claim 1, characterized in that: The step of performing transmission enhancement processing on the fused image to obtain an enhanced image comprises: Calculate the fused image The initial transmittance : ； In the formula, represents the correction factor, represents the local area in the fused image, Represents the image of one of the RGB channels of the fused image. Indicates the atmospheric light value of one of the RGB channels; The initial transmittance Perform update processing to obtain updated transmittance : ； In the formula, Respectively represent the image of one of the RGB channels of the fused image; Based on the updated transmittance The fused image Perform guided filtering to obtain guided transmittance : ； In the formula, represents the guided filter function, represents the guided filter window, represents the regularization factor; Based on the guided transmittance The fused image Perform enhancement processing to obtain an enhanced image : ； In the formula, Represents the intensity of light in the fused image.

7. An exposure image enhancement system, characterized in that: The system comprises: A preprocessing module is used to obtain an exposure image to be processed and preprocess the exposure image to be processed to obtain a processed image; A reconstruction module, used for performing Laplace reconstruction processing on the processed image to obtain a reconstructed image; A filtering module, used for performing a de-artifacting filtering process on the reconstructed image to obtain a filtered image; A fusion module, used for performing enhanced fusion processing on the filtered image to obtain a fused image; The enhancement module is used to perform transmission enhancement processing on the fused image to obtain an enhanced image.

8. The exposure image enhancement system according to claim 1, characterized in that: The reconstruction module includes: The down sampling submodule is used to construct a Gaussian pyramid, input the processed image into the Gaussian pyramid, and perform down sampling on the Gaussian pyramid. Down-sampling the processed image of the layer to obtain a sampled image; Gaussian submodule, used to pass the Gaussian pyramid The layer is subjected to Gaussian filtering to obtain a Gaussian filtered image; The repeating submodule is used to sequentially perform downsampling and Gaussian filtering on the Gaussian pyramid image to obtain An image of a pyramid; The upsampling submodule is used to The pyramid image is upsampled to obtain an upsampled image. The upsampled image and The Laplace difference operation is performed on the pyramid image to obtain Reconstructed image.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the exposure image enhancement method according to any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the exposure image enhancement method according to any one of claims 1 to 6 is implemented.

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