Adaptive multi-exposure fusion method and system based on structured light three-dimensional measurement

Abstract

The application discloses a kind of self-adapting multiple exposure fusion methods and systems based on surface structure light three-dimensional measurement, the method is first based on initial exposure image calculation modulation chart and maximum gray value, and according to modulation degree division multiple regions, and through the gray value correction and local information entropy weighting of pixel, the average gray of each clustering is accurately calculated, and then the optimal exposure time is solved for different regions, in image fusion stage, by analyzing the gray value distribution of high exposure image Dynamic determination of overexposure threshold, and with the pixel modulation degree under each exposure as the core basis, the fusion weight is calculated, and finally the global high-quality fringe image is generated.The application solves the problem that the reconstruction accuracy is low due to local overexposure or underexposure of high dynamic range objects, and significantly improves the three-dimensional measurement accuracy and automation level of high dynamic range objects.

Description

Technical Field

[0001] This invention relates to the field of grating projection 3D inspection technology, and in particular to an adaptive multiple exposure fusion method and system based on surface structured light 3D measurement. Background Technology

[0002] Structured light 3D measurement technology, especially based on digital fringe projection, has been widely used in industrial inspection, reverse engineering, and cultural heritage protection due to its advantages of high precision, non-contact operation, and high efficiency. This technology typically projects a sinusoidal fringe pattern onto an object, captures an image of the fringe modulated on the object's surface using a camera, and then reconstructs the object's 3D shape using algorithms such as phase resolution.

[0003] However, in practical applications, the surfaces of the objects being measured often possess complex reflective characteristics. For example, a circuit board may have near-specular reflective solder joints, diffusely reflective chip surfaces, and light-absorbing substrates. When using a single camera exposure time, a longer exposure time is often required to make the fringes in weakly reflective areas (such as a black substrate) clearly visible. However, this leads to severe pixel saturation in highly reflective areas (such as solder joints), a phenomenon known as "overexposure." Conversely, if a shorter exposure time is used to avoid overexposure in highly reflective areas, the fringe signal in weakly reflective areas will be very weak, resulting in an extremely low signal-to-noise ratio, a phenomenon known as "underexposure." Both overexposure and underexposure disrupt the sinusoidal nature of the fringes, making it impossible to accurately extract phase information and ultimately severely affecting the accuracy and completeness of 3D reconstruction.

[0004] To address this high dynamic range (HDR) issue, existing techniques typically employ a multiple exposure fusion strategy. However, these methods suffer from the following problems:

[0005] 1) Manually setting the exposure time cannot adapt to uneven lighting on the object surface (such as overexposure and loss of stripes in highlight areas, and blurry stripes in shadow areas).

[0006] 2) Traditional fusion methods do not consider stripe modulation (a core indicator reflecting stripe quality), resulting in a decrease in stripe contrast after fusion;

[0007] 3) The camera's nonlinear grayscale response was not calibrated, or the local information entropy was not used to distinguish important areas, resulting in underexposure of key details (such as defect edges). Summary of the Invention

[0008] The main objective of this invention is to provide an adaptive multiple exposure fusion method and system based on three-dimensional measurement using surface structured light, which can generate high-quality global stripe images and avoid overexposure or underexposure.

[0009] The technical solution adopted in this invention is:

[0010] An adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light is provided, comprising the following steps:

[0011] S1. Based on the preset initial exposure time The camera is controlled to acquire a set of initial images containing multi-step phase-shift fringes; based on this set of initial images, the modulation image M covering the entire scene and the maximum grayscale image are calculated. ;

[0012] S2. Divide all pixels of the modulated image into multiple regions according to their modulation values, and extract the image of the maximum gray value corresponding to the pixels in each region. The grayscale values ​​in the image are corrected by using the information entropy of the local neighborhood of the pixel as the weight of the pixel, and calculating the weighted average grayscale value of each region. ;

[0013] S3. Based on the weighted average gray value of each region Preset ideal grayscale value and initial exposure time The optimal exposure time for each region is calculated.

[0014] S4. Using the optimal exposure times of at least two regions as the long and short exposure times respectively, acquire stripe images to obtain a high-exposure image set. and low-exposure image set Calculate the corresponding high-exposure modulation charts respectively. Low-exposure tonal chart ;

[0015] S5. Based on the high-exposure image set The grayscale value distribution is used to adaptively calculate the overexposure threshold for the current scene. For each pixel in the current scene, determine whether it is in the high-exposure image set. Overexposure and its high-exposure modulation system diagram Low-exposure tonal system diagram The modulation index is used to calculate the fusion weight between the high and low exposure image sets; this fusion weight is then applied to the high exposure image set. and low-exposure image set A weighted average is then performed to generate the final fused stripe image.

[0016] Following the above technical solution, in step S2, the K-Means clustering algorithm is applied to the modulation image M to divide all pixels into K clusters based on their modulation values; each cluster... For a given region, calculate its weighted average gray value. , where k=1,...,K, and K is an integer.

[0017] Following the above technical solution, step S2 specifically involves analyzing the image of the maximum grayscale value corresponding to each pixel in each region. Gamma correction is performed on the grayscale values.

[0018] Following the above technical solution, in step S5, specifically based on the high-exposure image set... The cumulative distribution of the grayscale histogram is used to adaptively calculate the overexposure threshold for the current scene. .

[0019] Following the above technical solution, in step S2, the information entropy of the local neighborhood of the pixel... Calculated using the following formula: ,in It is the probability of the i-th gray level appearing within a neighborhood window of a preset size around the pixel, where i is an integer.

[0020] Following the above technical solution, the information entropy of the local neighborhood of a pixel After normalization, the values ​​are used as weights.

[0021] Following the above technical solution, the optimal exposure time , The preset ideal grayscale value The value after gamma correction.

[0022] Following the above technical solution, in step S5, the overexposure threshold is... To make the high-exposure image (1 P) represents the percentage of pixels with gray levels below this value, where P is a small percentage close to 0.

[0023] Following the above technical solution, in step S5, the high-exposure image set Low-exposure image set Fusion weights , The calculation method is as follows: = / ( + ) ; = / ( + ).

[0024] Following the above technical solution, in step S5, for pixels that are overexposed in a high-exposure image, their high-exposure weight is... Set to 0, low exposure weight Set it to 1.

[0025] The present invention also provides an adaptive multiple exposure fusion system based on three-dimensional measurement using surface structured light, comprising:

[0026] The initial image acquisition module is used to acquire images based on a preset initial exposure time. The camera is controlled to acquire a set of initial images containing multi-step phase-shift fringes; based on this set of initial images, the modulation image M covering the entire scene and the maximum grayscale image are calculated. ;

[0027] The partitioning module is used to divide all pixels of a modulated image into multiple regions based on their modulation values, and to extract the image with the maximum gray value corresponding to the pixels in each region. The grayscale values ​​in the image are corrected by using the information entropy of the local neighborhood of the pixel as the weight of the pixel, and calculating the weighted average grayscale value of each region. ;

[0028] The area exposure time calculation module is used to calculate the weighted average gray value of each area. Preset ideal grayscale value and initial exposure time The optimal exposure time for each region is calculated.

[0029] The fusion weight calculation module is used to acquire stripe images by using the optimal exposure times of at least two regions as long and short exposure times, respectively, to obtain a high-exposure image set. and low-exposure image set Calculate the corresponding high-exposure modulation charts respectively. Low-exposure tonal system diagram Based on high-exposure image sets The grayscale value distribution is used to adaptively calculate the overexposure threshold for the current scene. For each pixel in the current scene, determine whether it is in the high-exposure image set. Overexposure and its high-exposure modulation system diagram Low-exposure tonal system diagram The modulation index is used to calculate the fusion weight between high and low exposure image sets;

[0030] The image fusion module is used to apply the fusion weights to the high-exposure image set. and low-exposure image set A weighted average is then performed to generate the final fused stripe image.

[0031] The beneficial effects of this invention are as follows: This invention accurately calculates the average gray level of each region through grayscale correction and information entropy weighting, thereby solving for the optimal exposure time for different regions. In the image fusion stage, the overexposure threshold is dynamically determined by analyzing the grayscale values ​​of the high-exposure image, and the fusion weight is calculated based on the pixel modulation degree under each exposure, ultimately generating a high-quality global stripe image. This method significantly improves the accuracy and automation level of 3D measurement of high dynamic range objects. This invention makes the calculation of regional exposure time more accurate, ensuring the quality of subsequently acquired stripe images. Simultaneously, by using a fusion strategy to improve modulation degree as the ultimate goal, it directly serves the accuracy of 3D reconstruction, effectively solving the measurement problem of high dynamic range objects. This ensures that the final obtained 3D point cloud maintains good data integrity in both highlight and shadow areas, avoiding data gaps caused by overexposure or underexposure.

[0032] Furthermore, this invention achieves fully automatic optimization of exposure parameters without manual intervention and can intelligently adapt to the reflective characteristics of different objects.

[0033] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of the method for adaptive multiple exposure fusion system based on three-dimensional measurement of surface structured light according to an embodiment of the present invention;

[0036] Figure 2 This is a photograph of the reflective block taken in an embodiment of the present invention;

[0037] Figure 3 This is the maximum grayscale image of an embodiment of the present invention;

[0038] Figure 4 This is a modulation diagram of an embodiment of the present invention;

[0039] Figure 5 This is a K-Means clustering diagram from an embodiment of the present invention;

[0040] Figure 6 This is an overexposure mask image from an embodiment of the present invention;

[0041] Figure 7 This is a high-exposure fusion weight map according to an embodiment of the present invention;

[0042] Figure 8 This is a low-exposure fusion weight map according to an embodiment of the present invention;

[0043] Figure 9 This is the fused stripe pattern according to an embodiment of the present invention;

[0044] Figure 10 Point cloud images of reflective blocks taken without using this method;

[0045] Figure 11 This is a point cloud image of a reflective block taken using this method in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0047] It should be noted that the illustrations provided in the embodiments of the present invention are only schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0048] In this invention, it should also be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used only for descriptive and distinguishing purposes and should not be construed as indicating or implying relative importance.

[0049] Furthermore, it should be noted that the features of the various embodiments of the present invention can be combined or integrated in whole or in part, and as those skilled in the art will understand, they can interact and operate in different ways. Each embodiment can be implemented independently of each other or in association with one another.

[0050] The adaptive multiple exposure fusion method based on surface structured light 3D measurement in this invention aims to solve the problem of low reconstruction accuracy of high dynamic range objects due to local overexposure or underexposure. For example... Figure 1 As shown, the method mainly includes the following steps:

[0051] S1. Based on the preset initial exposure time The camera is controlled to acquire a set of initial images containing multi-step phase-shift fringes; based on this set of initial images, the modulation image M covering the entire scene and the maximum grayscale image are calculated. ;

[0052] S2. Divide all pixels of the modulated image into multiple regions according to their modulation values, and extract the image of the maximum gray value corresponding to the pixels in each region. The grayscale values ​​in the image are corrected by using the information entropy of the local neighborhood of the pixel as the weight of the pixel, and calculating the weighted average grayscale value of each region. ;

[0053] S3. Based on the weighted average gray value of each region Preset ideal grayscale value and initial exposure time The optimal exposure time for each region is calculated.

[0054] S4. Using the optimal exposure times of at least two regions as the long and short exposure times respectively, acquire stripe images to obtain a high-exposure image set. and low-exposure image set Calculate the corresponding high-exposure modulation charts respectively. Low-exposure tonal system diagram ;

[0055] S5. Based on the high-exposure image set The grayscale value distribution is used to adaptively calculate the overexposure threshold for the current scene. For each pixel in the current scene, determine whether it is in the high-exposure image set. Overexposure and its high-exposure modulation system diagram Low-exposure tonal system diagram The modulation index is used to calculate the fusion weight between the high and low exposure image sets; this fusion weight is then applied to the high exposure image set. and low-exposure image set A weighted average is then performed to generate the final fused stripe image. This merges images acquired under different exposures into a single image with high-quality stripes across all regions.

[0056] In one embodiment of the present invention, an adaptive multiple exposure fusion system based on surface structured light three-dimensional measurement can be pre-built. The system includes an industrial camera, a projector, a reflector and a calibration board. The camera can be a Daheng Mercury series MER2-1220-32U3C with a resolution of 4024×3036. The projector can be a DLP4500 manufactured by TI in the United States with a resolution of 912×1140. The object to be measured is the reflector.

[0057] In step S1, firstly, an initial exposure time is used. (For example, 2000 microseconds) A series of N-step phase-shift fringe patterns are captured and stored in an image queue. This process will generate a modulation map M and a maximum grayscale image. As shown in the attached image Figure 3 and Figure 4 As shown. For each pixel (row, col), its modulation degree is calculated using the following phase shift algorithm formula:

[0058]

[0059]

[0060]

[0061] in (row,col) is the gray value of the i-th phase-shifted image at point (row,col); A represents the in-phase component of the original fringe signal amplitude; B represents the quadrature component of the original fringe signal amplitude. Simultaneously, the image with the maximum gray value is calculated. In this context, the value of each pixel is the highest grayscale value that has appeared at the corresponding point in the multi-step phase-shifted image.

[0062] In step S2, the K-Means clustering algorithm can be applied to divide all pixels in the modulation map M into K (e.g., 3, representing high / medium / low modulation regions) clusters based on their modulation values, such as... Figure 5 As shown, each pixel is assigned a cluster ID, resulting in a cluster label matrix. This invention innovatively uses local information entropy to weight pixels to optimize exposure calculation. The principle is that regions with high information entropy correspond to rich textured key details, which are crucial for reconstruction accuracy. By assigning higher weights to these "key pixels," the optimal exposure time calculated by this method prioritizes ensuring the imaging quality of these regions, thereby improving the overall accuracy of the 3D model. To improve computational efficiency, a gamma correction lookup table is first created. The index in the table is value In this embodiment, considering the unique photoelectric response characteristics of the camera used, a gamma value of 0.6 was experimentally selected to achieve the best grayscale linearization effect. Then, each pixel in the image was traversed to obtain its cluster ID. If the modulation index of that pixel is greater than a preset threshold (e.g., 0.5, indicating a valid signal), its maximum grayscale value was extracted. The corresponding 3×3 neighborhood is then used to calculate the local information entropy of this region using the Shannon entropy formula. :

[0063]

[0064] in It is the probability of the i-th gray level appearing in the neighborhood. The calculated... Normalization is performed (e.g., divided by the theoretical maximum value of 8.0), and a minimum weight is set (e.g., 0.1) to obtain the final information entropy weight. From the lookup table Obtain the gamma-corrected grayscale value of the pixel. ,Will × The results are accumulated into the weighted total value of the cluster to which the pixel belongs. In the middle, at the same time Total weight added to the cluster middle.

[0065] In step S3, for each cluster k, its weighted average gray level is... Calculated using the following formula:

[0066]

[0067] Preset ideal grayscale value (For example, 250) also undergoes the same gamma correction to obtain the target grayscale value. Ultimately, the ideal exposure time for this cluster was determined by... The following formula determines:

[0068]

[0069] All the calculated K exposure times will form an optimal set of exposure times.

[0070] In step S4, although three optimal exposure times are calculated, the preferred embodiment of the present invention only integrates short exposures. He Chang's exposure Two exposures. This method is the most efficient. Ensure that the highlight areas are not saturated. Ensure sufficient signal in the dark areas. Fusing these two extreme exposures is sufficient to cover the full dynamic range, while increasing the intermediate exposure significantly increases time and computational cost, making it less practical. Assume that the two exposure times calculated in step 3 (one long exposure) have already been used. A short exposure Two sets of stripe patterns were captured, forming high-exposure image sets. and low-exposure image set Then, the modulation plots of the high-exposure and low-exposure image sets were calculated separately. and .

[0071] In step S5: It should be noted that the threshold used in this step to determine whether a pixel is overexposed is... This is dynamically calculated based on real-time data from the current high-exposure image, aiming to accurately identify saturated pixels. This is consistent with the preset ideal grayscale value used in step S3 to calculate the target exposure time. The function and definition are different; the latter is a fixed optimization objective. For high-exposure image sets... The grayscale histogram and cumulative distribution function of the first image are calculated. The grayscale level that results in a cumulative probability of 99.5% is found and used as the dynamic overexposure threshold. To increase stability, it can be... The value is limited to the range [200, 250].

[0072] Create an overexposure mask ,like Figure 6 As shown. If a pixel is in The grayscale value of any image exceeds Then in The corresponding position is marked as 1. All pixels are traversed to determine their fusion weights. and If the point is in If the value is 1 (overexposure), then the weight is set as follows: =0, =1. If not overexposed, the weight is calculated based on the modulation index: .like + =0, then = =0.5. The weight map can be normalized to ensure that at each pixel... + =1, such as Figure 7 and Figure 8 As shown. Finally, the two sets of stripe patterns are weighted and merged one by one, as follows. Figure 9 As shown. For the i-th pair of stripes, the pixel values ​​of the fused image are:

[0073]

[0074] All fused high-quality stripe patterns are pushed back into the image queue for use by subsequent 3D reconstruction algorithms.

[0075] In an embodiment of the present invention, Figure 2 This is a photograph of the reflective block taken in an embodiment of the present invention; Figure 10 Point cloud images of reflective blocks taken without using this method; Figure 11 The point cloud image of reflective blocks captured using this method in the embodiments of the present invention has obvious advantages.

[0076] To implement the above method embodiments, the present invention also provides an adaptive multiple exposure fusion system based on three-dimensional measurement of surface structured light, which mainly includes:

[0077] The initial image acquisition module is used to acquire images based on a preset initial exposure time. The camera is controlled to acquire a set of initial images containing multi-step phase-shift fringes; based on this set of initial images, the modulation image M covering the entire scene and the maximum grayscale image are calculated. ;

[0078] The partitioning module is used to divide all pixels of a modulated image into multiple regions based on their modulation values, and to extract the image with the maximum gray value corresponding to the pixels in each region. The grayscale values ​​in the image are corrected by using the information entropy of the local neighborhood of the pixel as the weight of the pixel, and calculating the weighted average grayscale value of each region. ;

[0079] The area exposure time calculation module is used to calculate the weighted average gray value of each area. Preset ideal grayscale value and initial exposure time The optimal exposure time for each region is calculated.

[0080] The fusion weight calculation module is used to acquire stripe images by using the optimal exposure times of at least two regions as long and short exposure times, respectively, to obtain a high-exposure image set. and low-exposure image set Calculate the corresponding high-exposure modulation charts respectively. Low-exposure tonal system diagram Based on high-exposure image sets The grayscale value distribution is used to adaptively calculate the overexposure threshold for the current scene. For each pixel in the current scene, determine whether it is in the high-exposure image set. Overexposure and its high-exposure modulation system diagram Low-exposure tonal system diagram The modulation index is used to calculate the fusion weight between high and low exposure image sets;

[0081] The image fusion module is used to apply the fusion weights to the high-exposure image set. and low-exposure image set A weighted average is then performed to generate the final fused stripe image.

[0082] Each module is mainly used to implement the various steps of the above method embodiments, which will not be described in detail here.

[0083] This application also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc., which stores a computer program, and the program implements corresponding functions when executed by a processor. In this embodiment, the computer-readable storage medium, when executed by a processor, implements the adaptive multiple exposure fusion method based on surface structured light three-dimensional measurement, as described in the method embodiment.

[0084] In summary, this invention, through a tightly coupled automatic exposure calculation and adaptive fusion algorithm, can provide high-quality raw data for structured light systems under various complex lighting conditions and object surfaces, and is a key technology for achieving high-precision and high-robustness three-dimensional measurement.

[0085] The present invention relates to an adaptive multiple exposure fusion method and system based on three-dimensional measurement using surface structured light. The system mainly includes an industrial camera, a projector, a reflector, a computer, and a calibration plate. The system realizes the tightly coupled automatic exposure calculation and adaptive fusion algorithm of the above method embodiment, which will not be elaborated here.

[0086] In summary, the adaptive multiple exposure fusion method and system based on structured light 3D measurement of this invention can effectively generate point cloud data of reflective blocks with accurate 3D coordinates. The core advantage of this method lies in solving the problem of severe degradation in 3D reconstruction accuracy caused by local overexposure or underexposure in traditional single-exposure methods when dealing with objects with high dynamic range. By performing cluster analysis on the modulation of the initial image and innovatively introducing weighted grayscale based on local information entropy and correction to automatically calculate multiple sets of optimal exposure times, combined with a fusion strategy based on modulation and adaptive overexposure threshold, this invention can generate a perfect image that retains clear fringe details in any region (whether highlight or shadow). This high-quality raw data is the cornerstone of accurate 3D reconstruction. Therefore, by optimizing the quality of the input image, this invention greatly improves the accuracy and stability of 3D shape measurement of precision objects such as reflective blocks, providing strong technical support for quality control and defect detection of high-contrast objects.

[0087] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0088] The order of the steps in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0089] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. An adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light, characterized in that, Includes the following steps: S1. Based on the preset initial exposure time The camera is controlled to acquire a set of initial images containing multi-step phase-shift fringes; based on this set of initial images, the modulation image M covering the entire scene and the maximum grayscale image are calculated. ; S2. Divide all pixels of the modulated image into multiple regions according to their modulation values, and extract the image of the maximum gray value corresponding to the pixels in each region. The grayscale values ​​in the image are corrected by using the information entropy of the local neighborhood of the pixel as the weight of the pixel, and calculating the weighted average grayscale value of each region. ; S3. Based on the weighted average gray value of each region Preset ideal grayscale value and initial exposure time The optimal exposure time for each region is calculated. S4. Using the optimal exposure times of at least two regions as the long and short exposure times respectively, acquire stripe images to obtain a high-exposure image set. and low-exposure image set Calculate the corresponding high-exposure modulation charts respectively. Low-exposure tonal system diagram ; S5. Based on the high-exposure image set The grayscale value distribution is used to adaptively calculate the overexposure threshold for the current scene. For each pixel in the current scene, determine whether it is in the high-exposure image set. Overexposure and its high-exposure modulation system diagram Low-exposure tonal system diagram The modulation index is used to calculate the fusion weight between the high and low exposure image sets; this fusion weight is then applied to the high exposure image set. and low-exposure image set A weighted average is then performed to generate the final fused stripe image.

2. The adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light according to claim 1, characterized in that, In step S2, the K-Means clustering algorithm is applied to the modulation image M to divide all pixels into K clusters based on their modulation values; each cluster... For a given region, calculate its weighted average gray value. , where k=1,...,K, and K is an integer.

3. The adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light according to claim 1, characterized in that, In step S2, the image of the maximum grayscale value corresponding to each pixel in each region is specifically generated. Gamma correction is performed on the grayscale values.

4. The adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light according to claim 1, characterized in that, In step S5, specifically based on the high-exposure image set The cumulative distribution of the grayscale histogram is used to adaptively calculate the overexposure threshold for the current scene. .

5. The adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light according to claim 1, characterized in that, In step S2, the information entropy of the local neighborhood of the pixel. Calculated using the following formula: ,in It is the probability of the i-th gray level appearing within a neighborhood window of a preset size around the pixel, where i is an integer; the information entropy of the local neighborhood of the pixel. After normalization, the values ​​are used as weights.

6. The adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light according to claim 5, characterized in that, Optimal exposure time , The preset ideal grayscale value The value after gamma correction.

7. The adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light according to claim 1, characterized in that, In step S5, the overexposure threshold is... To make the high-exposure image (1 P) represents the percentage of pixels with gray levels below this value, where P is a small percentage close to 0.

8. The adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light according to claim 1, characterized in that, In step S5, the high-exposure image set Low-exposure image set Fusion weights , The calculation method is as follows: = / ( + ); = / ( + ).

9. The adaptive multiple exposure fusion method based on three-dimensional measurement using surface structured light according to any one of claims 1-8, characterized in that, In step S5, for pixels that are overexposed in a high-exposure image, their high-exposure weight is... Set to 0, low exposure weight Set it to 1.

10. An adaptive multiple exposure fusion system based on three-dimensional measurement using surface structured light, characterized in that, include: The initial image acquisition module is used to acquire images based on a preset initial exposure time. The camera is controlled to acquire a set of initial images containing multi-step phase-shift fringes; based on this set of initial images, the modulation image M covering the entire scene and the maximum grayscale image are calculated. ; The partitioning module is used to divide all pixels of a modulated image into multiple regions based on their modulation values, and to extract the image with the maximum gray value corresponding to the pixels in each region. The grayscale values ​​in the image are corrected by using the information entropy of the local neighborhood of the pixel as the weight of the pixel, and calculating the weighted average grayscale value of each region. ; The area exposure time calculation module is used to calculate the weighted average gray value of each area. Preset ideal grayscale value and initial exposure time The optimal exposure time for each region is calculated. The fusion weight calculation module is used to acquire stripe images by using the optimal exposure times of at least two regions as long and short exposure times, respectively, to obtain a high-exposure image set. and low-exposure image set Calculate the corresponding high-exposure modulation charts respectively. Low-exposure tonal system diagram Based on high-exposure image sets The grayscale value distribution is used to adaptively calculate the overexposure threshold for the current scene. For each pixel in the current scene, determine whether it is in the high-exposure image set. Overexposure and its high-exposure modulation system diagram Low-exposure tonal system diagram The modulation index is used to calculate the fusion weight between high and low exposure image sets; The image fusion module is used to apply the fusion weights to the high-exposure image set. and low-exposure image set A weighted average is then performed to generate the final fused stripe image.

Images