A Single-Frame Gamma Correction Method and System Based on Multi-Region Response Weighted Fitting

Abstract

This disclosure relates to a single-frame gamma correction method and system based on multi-region response weighted fitting, comprising: acquiring a projected image captured by a camera, the projected image containing four gray levels and horizontally divided into eight regions; selecting multiple pixels at the center of each region, calculating their average gray value as the measured output value of that region, pairing the calibrated gray value with the corresponding measured output gray value, fitting the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain a symmetrical region combination and its initial gamma value; weighting and fusing the initial gamma values ​​of the symmetrical region combination to obtain a fused gamma value, and substituting the fused gamma value into the nonlinear response model to obtain nonlinear correction parameters; obtaining compensation coefficients for image preprocessing based on the nonlinear correction parameters, and preprocessing the sinusoidal fringe image using the compensation coefficients to obtain a corrected ideal sinusoidal fringe image. This method can effectively improve correction efficiency and robustness.

Description

Technical Field

[0001] This disclosure relates to the field of computer vision, and in particular to a single-frame gamma correction method and system based on multi-region response weighted fitting. Background Technology

[0002] In recent years, fringe projection profilometry has been widely applied in fields such as industrial manufacturing, biomedicine, cultural relic preservation, and virtual reality due to its non-contact, high-speed, and high-precision characteristics. However, with the diversification of application scenarios, the environment for 3D measurement is becoming increasingly complex, especially the problems of highlights and underexposure. Highlights are easily generated on metallic or smooth surfaces, leading to the loss of fringe data and the appearance of ripples and holes in the reconstructed point cloud; while underexposure of dark objects often results in excessive noise and loss of detail. In actual measurements, these two situations often coexist. Therefore, there is an urgent need for a more adaptable high dynamic range imaging method to solve these problems.

[0003] Existing high dynamic range (HDR) 3D measurement techniques, such as multi-exposure fusion, projection intensity adjustment, polarizer filtering, photometric stereo methods, and deep learning-based methods, all have their limitations. For example, multi-exposure fusion requires extensive image reconstruction, resulting in low efficiency; adjusting projection intensity requires preprocessing based on different scenes, also leading to low efficiency; polarizer filtering can darken images, reducing the signal-to-noise ratio; photometric stereo methods require additional light sources to suppress highlights, narrowing the field of view; and end-to-end deep learning-based methods require large amounts of training data, are time-consuming, and have limited generalization performance. To address these issues, a new 3D measurement technique is needed that can simultaneously measure high-exposure and low-exposure areas in a single exposure without compromising measurement accuracy, thus meeting the rapidly evolving demands of 3D measurement. Summary of the Invention

[0004] To address the problems of complex iterative calculations, strong dependence on multiple frames, and limited correction accuracy in traditional gamma correction methods when dealing with the nonlinear response of projectors, this disclosure proposes a single-frame gamma correction method based on multi-region response weighted fitting to solve these problems.

[0005] According to one aspect of this disclosure, a single-frame gamma correction method based on multi-region response weighted fitting is provided, comprising:

[0006] S10. Acquire the projected image captured by the camera. The projected image contains four gray levels and is divided into eight regions horizontally. The left half of the projected image is linearly arranged in ascending order of the calibrated gray values ​​in four regions. The calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial positions are randomly distributed in the other four regions.

[0007] S20. Select multiple pixels at the center of each region of the projected image, calculate their average gray value as the measured output value of the region, pair the calibrated gray value with the corresponding measured output gray value, and fit the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain the symmetrical region combination and its initial gamma value.

[0008] S30. The initial gamma values ​​of the symmetrical region combination are weighted and fused to obtain the fused gamma value, and the fused gamma value is substituted into the nonlinear response model to obtain the nonlinear correction parameter.

[0009] S40. Based on the nonlinear correction parameters, calculate the compensation coefficient for image preprocessing, and preprocess the sinusoidal fringe image using the compensation coefficient to obtain the corrected ideal sinusoidal fringe image.

[0010] Preferably, the left half of the projected image is linearly arranged in ascending order of the calibrated gray values ​​across four regions, and the calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial positions are randomly distributed across the other four regions, including:

[0011] The calibrated gray values ​​of the four regions in the left half of the projected image are represented as follows:

[0012] ,

[0013] Since the calibrated grayscale values ​​of the right half of the projected image are the same as those of the left half, the calibrated grayscale values ​​of the four regions in the right half of the projected image are represented as follows:

[0014] ,

[0015] In the formula, The left half consists of four regions arranged linearly in ascending order of their calibrated grayscale values. This corresponds to the right half of the area.

[0016] Preferably, the initial gamma values ​​of the symmetrical region combination are weighted and fused to obtain the fused gamma value, expressed as:

[0017] ,

[0018] In the formula, To integrate gamma values, The initial gamma value, Use Gaussian weights.

[0019] Preferably, the fused gamma value is substituted into the nonlinear response model to obtain the nonlinear correction parameter, which is expressed as:

[0020] ,

[0021] In the formula, , These are nonlinear correction parameters. , To integrate gamma values, , These are the gamma precoding values ​​corresponding to the two sets of grayscale combination regions.

[0022] Preferably, based on the nonlinear correction parameters, the compensation coefficients for image preprocessing are calculated and expressed as follows:

[0023] ,

[0024] In the formula, These are the compensation coefficients for image preprocessing.

[0025] Preferably, the sinusoidal fringe image is preprocessed using a compensation coefficient to obtain a corrected ideal sinusoidal fringe image, including:

[0026] The intensity of each pixel in the sinusoidal fringe image is non-linearly adjusted using a compensation coefficient.

[0027] By precoding and compensating the nonlinearly adjusted sinusoidal fringe image using a distortion model, a corrected ideal sinusoidal fringe image is obtained.

[0028] Preferably, the intensity of each pixel in the sinusoidal fringe image is non-linearly adjusted using a compensation coefficient, wherein the intensity of each pixel in the sinusoidal fringe image is expressed as:

[0029] ,

[0030] ,

[0031] In the formula, Represents the coordinates of a pixel. The initial intensity of the sinusoidal fringe image is given. The modulation intensity of the sinusoidal fringe image. This represents the initial phase of the sinusoidal fringe image. This represents the phase offset.

[0032] According to one aspect of this disclosure, a single-frame gamma correction system based on multi-region response weighted fitting is provided, comprising:

[0033] The projected image acquisition module acquires the projected image captured by the camera. The projected image contains four gray levels and is divided into eight regions horizontally. The left half of the projected image is linearly arranged in ascending order of the calibrated gray values ​​in four regions. The calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial positions are randomly distributed in the other four regions.

[0034] The initial gamma value calculation module selects multiple pixels at the center of each region of the projected image, calculates their average gray value as the measured output value of that region, pairs the calibrated gray value with the corresponding measured output gray value, and fits the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain the symmetrical region combination and its initial gamma value.

[0035] The gamma value weighted fusion module weights and fuses the initial gamma values ​​of the symmetrical region combination to obtain a fused gamma value, and substitutes the fused gamma value into the nonlinear response model to obtain the nonlinear correction parameters.

[0036] The ideal sinusoidal fringe image correction module calculates the compensation coefficient for image preprocessing based on the nonlinear correction parameters, and preprocesses the sinusoidal fringe image using the compensation coefficient to obtain the corrected ideal sinusoidal fringe image.

[0037] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: perform the above-described single-frame gamma correction method based on multi-region response weighted fitting.

[0038] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the above-described single-frame gamma correction method based on multi-region response weighted fitting.

[0039] Compared to the prior art, the beneficial effects of this disclosure are as follows:

[0040] 1) This disclosure uses a single-frame multi-grayscale precoding combined with a multi-region response weighted fitting method, which avoids the cumbersome process of traditional multi-frame acquisition and complex mechanical adjustment. High-precision gamma correction can be completed with only one projection and one shooting.

[0041] 2) This disclosure uses a response sampling and Gaussian weighted fusion algorithm based on the center and edge regions to accurately suppress uneven brightness of the projector and edge attenuation effect, thus providing a solid foundation for stable estimation of the fused gamma value.

[0042] 3) This disclosure combines the weighted fused gamma value with the compensation coefficient of image preprocessing to obtain a pre-coded correction result that has the dual advantages of fast single-frame speed and high precision. It can effectively linearize the output grayscale response, enhance details in dark and weak areas, and adapt to complex optical non-uniformity scenes to achieve reliable gamma compensation effect.

[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0044] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0046] Figure 1 A flowchart of a single-frame gamma correction method based on multi-region response weighted fitting is shown;

[0047] Figure 2 This shows a three-dimensional system schematic diagram of the single-frame gamma correction method based on multi-region response weighted fitting in this disclosure example;

[0048] Figure 3 A block diagram of a single-frame gamma correction system based on multi-region response weighted fitting is shown in an embodiment of this disclosure. Detailed Implementation

[0049] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0050] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0051] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0052] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0054] Example 1

[0055] Based on the above ideas, this invention proposes a single-frame gamma correction method based on multi-region response weighted fitting. Figure 1 A flowchart of a single-frame gamma correction method based on multi-region response weighted fitting is shown. The method includes:

[0056] S10. Acquire the projected image captured by camera 20. The projected image contains four gray levels and is divided into eight regions horizontally. The left half of the projected image is linearly arranged in ascending order of the calibrated gray values ​​in four regions. The calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial positions are randomly distributed in the other four regions.

[0057] S20. Select multiple pixels at the center of each region of the projected image, calculate their average gray value as the measured output value of the region, pair the calibrated gray value with the corresponding measured output gray value, and fit the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain the symmetrical region combination and its initial gamma value.

[0058] S30. The initial gamma values ​​of the symmetrical region combination are weighted and fused to obtain the fused gamma value, and the fused gamma value is substituted into the nonlinear response model to obtain the nonlinear correction parameter.

[0059] S40. Based on the nonlinear correction parameters, calculate the compensation coefficient for image preprocessing, and preprocess the sinusoidal fringe image using the compensation coefficient to obtain the corrected ideal sinusoidal fringe image.

[0060] This disclosure provides a single-frame gamma correction method based on multi-region response weighted fitting, specifically including the following steps:

[0061] S10. Acquire the projected image captured by camera 20. The projected image contains four gray levels and is divided into eight regions horizontally. The left half of the projected image is linearly arranged in ascending order of the calibrated gray values ​​in four regions. The calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial positions are randomly distributed in the other four regions.

[0062] In this embodiment, Figure 2 This diagram illustrates a three-dimensional system of the single-frame gamma correction method based on multi-region response weighted fitting, as described in this disclosure. A projector 30 projects a pre-coded image containing four gray levels onto the object 10 under test. A camera 20 captures the projected image in a single exposure. This projected image is horizontally divided into eight regions. The left half of the projected image consists of four regions arranged linearly in ascending order of their calibrated gray values. The calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial locations are randomly distributed across the other four regions. This ensures symmetrical grayscale input and asymmetrical spatial distribution.

[0063] The calibrated gray values ​​of the four regions in the left half of the projected image are represented as follows:

[0064] ,

[0065] Since the calibrated gray values ​​of the right half of the projected image are the same as those of the left half, the calibrated gray values ​​of the four regions in the right half of the projected image are represented as follows:

[0066] ,

[0067] In the formula, The left half consists of four regions arranged linearly in ascending order of their calibrated grayscale values. This corresponds to the right half of the area.

[0068] Select a base gray value To characterize the input brightness level of projector 30, take The average value of the region is used as the reference gray value. Then input grayscale value and reference gray value The functional relationship can be defined as:

[0069] ,

[0070] In the formula, This is a precoded value.

[0071] S20. Select multiple pixels at the center of each region of the projected image, calculate their average gray value as the measured output value of the region, pair the calibrated gray value with the corresponding measured output gray value, and fit the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain the symmetrical region combination and its initial gamma value.

[0072] In this embodiment, multiple pixels located at the center of each region are selected from the projected image captured by camera 20, and the average gray value of these pixels is calculated as the measured output value for that region. The relationship between the input light intensity and the obtained measured output value is fitted using an exponential function of a nonlinear response model, expressed as:

[0073] ,

[0074] In the formula, a、b For system parameters, To output light intensity.

[0075] S30. The initial gamma values ​​of the symmetrical region combination are weighted and fused to obtain the fused gamma value, and the fused gamma value is substituted into the nonlinear response model to obtain the nonlinear correction parameter.

[0076] In this embodiment, A response curve is obtained by plotting the gray values ​​of the region. Similarly, they are grouped by region. , , Construct a response curve to obtain the initial gamma value. , , For the initial gamma value of the symmetrical region, a Gaussian weight based on the distance between the region's center pixel and the image center is introduced. , and , A weighted average was then performed to obtain two more representative fused gamma values.

[0077] The initial gamma values ​​of the symmetrical region combination are weighted and fused to obtain the fused gamma value, which is expressed as:

[0078] ,

[0079] In the formula, To integrate gamma values, The initial gamma value, Use Gaussian weights.

[0080] Furthermore, substituting the fused gamma value into the nonlinear response model, we obtain the following equation:

[0081] ,

[0082] ,

[0083] By combining the projector's own gamma parameters with the weighted fused gamma value, the nonlinear correction parameters can be derived by solving simultaneous equations. , , represented as:

[0084] ,

[0085] In the formula, , These are nonlinear correction parameters. , To integrate gamma values, , These are the gamma precoding values ​​corresponding to the two sets of grayscale combination regions.

[0086] S40. Based on the nonlinear correction parameters, calculate the compensation coefficient for image preprocessing, and preprocess the sinusoidal fringe image using the compensation coefficient to obtain the corrected ideal sinusoidal fringe image.

[0087] In this embodiment, the compensation coefficient for image preprocessing is calculated based on the nonlinear correction parameter, and is expressed as follows:

[0088] ,

[0089] In the formula, These are the compensation coefficients for image preprocessing.

[0090] Furthermore, the sinusoidal fringe image is preprocessed using compensation coefficients to obtain a corrected ideal sinusoidal fringe image, including: nonlinearly adjusting the intensity of each pixel in the sinusoidal fringe image using compensation coefficients; and precoding and compensating the nonlinearly adjusted sinusoidal fringe image using a distortion model to obtain the corrected ideal sinusoidal fringe image.

[0091] Specifically, the intensity of each pixel in the sinusoidal fringe image is non-linearly adjusted using a compensation coefficient, and the intensity of each pixel in the sinusoidal fringe image generated by the computer 40 is expressed as follows:

[0092] ,

[0093] ,

[0094] In the formula, Represents the coordinates of a pixel. The initial intensity of the sinusoidal fringe image is given. The modulation intensity of the sinusoidal fringe image. This represents the initial phase of the sinusoidal fringe image. This represents the phase offset.

[0095] By precoding and compensating the nonlinearly adjusted sinusoidal fringe image using a distortion model, a corrected ideal sinusoidal fringe image is obtained. The gamma distortion intensity received by the actual camera 20 can be expressed as:

[0096] ,

[0097] In the formula, The DC component, For the first k The amplitude of the second harmonic component, is the gamma coefficient.

[0098] This embodiment uses multi-region response sampling combined with exponential function fitting to classify the input-output response curves of each grayscale region, and determines the optimal fusion weight by maximizing the weighted difference between the curves, thereby achieving a more accurate initial gamma value estimation. The embodiment also uses a Gaussian weighted fusion model, combined with the inverse equation between the inherent gamma of the projector 30 and the fused gamma value after weighted fusion, to calculate the nonlinear correction parameters with high precision, thereby accurately determining the compensation coefficients for image preprocessing.

[0099] Example 2

[0100] As another aspect of the embodiments of this disclosure, a single-frame gamma correction system 100 based on multi-region response weighted fitting is also provided, such as... Figure 3 As shown, it includes:

[0101] Projected image acquisition module 1 acquires the projected image captured by camera 20. The projected image contains four gray levels and is divided into eight regions horizontally. The left half of the projected image is linearly arranged in ascending order of the calibrated gray values ​​in four regions. The calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial positions are randomly distributed in the other four regions.

[0102] Initial gamma value calculation module 2 selects multiple pixels at the center of each region of the projected image, calculates their average gray value as the measured output value of that region, pairs the calibrated gray value with the corresponding measured output gray value, and fits the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain the symmetrical region combination and its initial gamma value.

[0103] The gamma value weighted fusion module 3 weights and fuses the initial gamma values ​​of the symmetrical region combination to obtain a fused gamma value, and substitutes the fused gamma value into the nonlinear response model to obtain the nonlinear correction parameter;

[0104] The ideal sinusoidal fringe image correction module 4 calculates the compensation coefficient for image preprocessing based on the nonlinear correction parameters, and preprocesses the sinusoidal fringe image using the compensation coefficient to obtain the corrected ideal sinusoidal fringe image.

[0105] Without causing contradictions, the above-described modules in the system of the present disclosure embodiments can implement any of the above-described methods.

[0106] Based on the description of the above embodiments, it can be seen that the embodiments of this disclosure can achieve the following technical effects:

[0107] 1) This disclosure uses a single-frame multi-grayscale precoding combined with a multi-region response weighted fitting method, which avoids the cumbersome process of traditional multi-frame acquisition and complex mechanical adjustment. High-precision gamma correction can be completed with only one projection and one shooting.

[0108] 2) This disclosure uses a response sampling and Gaussian weighted fusion algorithm based on the center and edge regions to accurately suppress the uneven brightness and edge attenuation effect of the projector, thus providing a solid foundation for the stable estimation of the fused gamma value.

[0109] 3) This disclosure combines the weighted fused gamma value with the compensation coefficient of image preprocessing to obtain a pre-coded correction result that has the dual advantages of fast single-frame speed and high precision. It can effectively linearize the output grayscale response, enhance details in dark and weak areas, and adapt to complex optical non-uniformity scenes to achieve reliable gamma compensation effect.

[0110] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to use the aforementioned single-frame gamma correction method based on multi-region response weighted fitting. The electronic device can be provided as a terminal, a server, or other type of device.

[0111] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the aforementioned single-frame gamma correction method based on multi-region response weighted fitting. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0112] Those skilled in the art will understand that, in the above-described single-frame gamma correction method and system based on multi-region response weighted fitting in specific implementations, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0114] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A single-frame gamma correction method based on multi-region response weighted fitting, characterized in that, Includes the following steps: S10. Acquire the projected image captured by the camera. The projected image contains four gray levels and is divided into eight regions horizontally. The left half of the projected image is linearly arranged in ascending order of the calibrated gray values ​​in four regions. The calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial positions are randomly distributed in the other four regions. S20. Select multiple pixels at the center of each region of the projected image, calculate their average gray value as the measured output value of the region, pair the calibrated gray value with the corresponding measured output gray value, and fit the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain the symmetrical region combination and its initial gamma value. S30. The initial gamma values ​​of the symmetrical region combination are weighted and fused to obtain a fused gamma value, and the fused gamma value is substituted into the nonlinear response model to obtain the nonlinear correction parameter; wherein, the fused gamma value is expressed as: ， In the formula, To integrate gamma values, The initial gamma value, Gaussian weights; The nonlinear correction parameter is expressed as: ， In the formula, , These are nonlinear correction parameters. , To integrate gamma values, , These are the gamma precoding values ​​corresponding to the two sets of grayscale combination regions; S40. Based on the nonlinear correction parameters, calculate the compensation coefficient for image preprocessing, and preprocess the sinusoidal fringe image using the compensation coefficient to obtain the corrected ideal sinusoidal fringe image; the compensation coefficient is expressed as: ， In the formula, These are the compensation coefficients for image preprocessing.

2. The method according to claim 1, characterized in that, The left half of the projected image consists of four regions arranged linearly in ascending order of their calibrated gray values. The right half of the projected image has the same calibrated gray values ​​as the left half, but its spatial location is randomly distributed across the other four regions, including: The calibrated gray values ​​of the four regions in the left half of the projected image are represented as follows: ， Since the calibrated grayscale values ​​of the right half of the projected image are the same as those of the left half, the calibrated grayscale values ​​of the four regions in the right half of the projected image are represented as follows: ， In the formula, The left half consists of four regions arranged linearly in ascending order of their calibrated grayscale values. This corresponds to the right half of the area.

3. The method according to claim 1, characterized in that, By preprocessing the sinusoidal fringe image using compensation coefficients, a corrected ideal sinusoidal fringe image is obtained, including: The intensity of each pixel in the sinusoidal fringe image is non-linearly adjusted using a compensation coefficient. By precoding and compensating the nonlinearly adjusted sinusoidal fringe image using a distortion model, a corrected ideal sinusoidal fringe image is obtained.

4. The method according to claim 3, characterized in that, The intensity of each pixel in the sinusoidal fringe image is nonlinearly adjusted by a compensation coefficient, and the intensity of each pixel in the sinusoidal fringe image is expressed as follows: ， ， In the formula, Represents the coordinates of a pixel. The initial intensity of the sinusoidal fringe image is given. The modulation intensity of the sinusoidal fringe image. This represents the initial phase of the sinusoidal fringe image. This represents the phase offset.

5. A single-frame gamma correction system based on multi-region response weighted fitting, characterized in that, include: The projected image acquisition module acquires the projected image captured by the camera. The projected image contains four gray levels and is divided into eight regions horizontally. The left half of the projected image is linearly arranged in ascending order of the calibrated gray values ​​in four regions. The calibrated gray values ​​of the right half of the projected image are the same as those of the left half, but their spatial positions are randomly distributed in the other four regions. The initial gamma value calculation module selects multiple pixels at the center of each region of the projected image, calculates their average gray value as the measured output value of that region, pairs the calibrated gray value with the corresponding measured output gray value, and fits the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain the symmetrical region combination and its initial gamma value. The gamma-value weighted fusion module weights and fuses the initial gamma values ​​of the symmetrical region combination to obtain a fused gamma value, and substitutes the fused gamma value into the nonlinear response model to obtain the nonlinear correction parameters; wherein, the fused gamma value is expressed as: ， In the formula, To integrate gamma values, The initial gamma value, Gaussian weights; The nonlinear correction parameter is expressed as: ， In the formula, , These are nonlinear correction parameters. , To integrate gamma values, , These are the gamma precoding values ​​corresponding to the two sets of grayscale combination regions; The ideal sinusoidal fringe image correction module calculates compensation coefficients for image preprocessing based on the nonlinear correction parameters, and preprocesses the sinusoidal fringe image using these compensation coefficients to obtain the corrected ideal sinusoidal fringe image; the compensation coefficients are expressed as: ， In the formula, These are the compensation coefficients for image preprocessing.

6. An electronic device 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, it implements the single-frame gamma correction method based on multi-region response weighted fitting as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the single-frame gamma correction method based on multi-region response weighted fitting as described in any one of claims 1 to 4.

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