A method and system for correcting image distortion of a fringe camera based on a space-time cross-point grid correction

Abstract

The present application relates to a kind of image processing method and system, specifically, a kind of stripe camera image distortion correction method and system based on space-time intersection point grid correction, the purpose is to solve the existing two-dimensional distortion information separation measurement cannot be fused in prior art, lack general correction matrix, resulting in low correction efficiency, and the problem of insufficient correction accuracy based on overall average parameter.Method includes:1) acquisition time, spatial direction distortion image;2) superimposed generation space-time intersection point grid image;3) extract all intersection point actual pixel coordinates in intersection point grid image;4) coordinate mapping, solve distortion correction matrix;5) using distortion correction matrix is transformed, interpolation, output correction image.The present application overcomes the defect that two kinds of distortion are separately processed in traditional method, information is missing, greatly improves correction efficiency, effectively improves correction accuracy.

Description

Technical Field

[0001] This invention relates to an image processing method and system, specifically to a method and system for correcting image distortion in a striped camera based on spatiotemporal intersection grid correction. Background Technology

[0002] Streak cameras are core equipment in ultrafast optical diagnostics and are widely used in fields such as inertial confinement fusion, plasma physics, and laser physics. Streak cameras record one-dimensional spatiotemporal evolution information of ultrafast processes by converting incident light signals into electronic signals, which are then scanned and deflected before being converted into visible light images by a fluorescent screen.

[0003] However, due to factors such as aberrations in the electro-optical system, nonlinearity of the scanning circuit, and non-ideal characteristics of the photocathode geometry, streak cameras exhibit two dimensions of image distortion under dynamic operating conditions: distortion in the temporal direction (scanning direction) is mainly manifested as nonlinear compression or stretching of the time axis caused by uneven scanning speed; distortion in the spatial direction (slit direction) is mainly manifested as bending and deformation of the slit image caused by aberrations in the electro-optical system and uneven magnification. These distortions severely affect the temporal and spatial resolution of the streak camera image, thus limiting measurement accuracy.

[0004] To address the aforementioned issues, researchers have proposed corresponding distortion measurement methods. For example, equally spaced pulse trains are used as a time reference to calibrate nonlinear distortion in the scanning direction, or patterns with equally spaced transmittance variations along the slit direction are used to assess geometric distortion in the spatial direction. Regarding distortion correction, some literature proposes scanning nonlinearity correction algorithms based on image processing, correcting pixel position and brightness by fitting the scanning speed curve; other studies employ nonlinear least squares methods to process scan speed calibration data, reducing the impact of scan speed nonlinearity to some extent.

[0005] However, the existing technologies still have the following shortcomings: First, temporal distortion and spatial distortion are usually measured and processed separately, failing to effectively integrate their distortion information, making it difficult to fully characterize two-dimensional coupled distortion; Second, existing methods lack the means to convert the measured discrete distortion information into a universal correction matrix that can be directly used for batch image correction, requiring repeated calibration for each correction, resulting in low efficiency; Third, traditional methods mostly adopt correction strategies based on overall average parameters, which cannot accurately reflect the distortion differences in different local areas of the camera, resulting in limited correction accuracy.

[0006] Therefore, there is an urgent need for a method and system that can organically integrate temporal and spatial distortion information to form a complete two-dimensional distortion mesh, and generate a universal correction matrix to achieve fast and high-precision distortion correction of subsequent images. Summary of the Invention

[0007] The purpose of this invention is to solve the problems in the prior art, such as the inability to fuse two-dimensional distortion information separation measurements, the lack of a universal correction matrix, resulting in low correction efficiency, and insufficient correction accuracy due to the overall average parameters. The invention provides a method and system for correcting image distortion of a striped camera based on spatiotemporal intersection grid correction.

[0008] To achieve the above objectives, the technical solution provided by this invention is as follows:

[0009] A method for correcting image distortion in a streak camera based on spatiotemporal intersection grid correction is characterized by the following steps:

[0010] Step 1: Under dynamic working conditions, acquire the temporal distortion image and spatial distortion image of the streak camera respectively;

[0011] Step 2: Superimpose and combine the temporal distortion image and the spatial distortion image to form an intersection grid image containing two-dimensional spatiotemporal distortion information;

[0012] Step 3: Extract the actual pixel coordinates of all intersection points in the intersection point grid image;

[0013] Step 4: Calculate the distortion correction matrix based on the preset mapping relationship between the ideal grid coordinates and the actual pixel coordinates;

[0014] Step 5: Use the distortion correction matrix to perform pixel-by-pixel coordinate transformation and grayscale interpolation on all subsequent original images captured by the stripe camera, and output the corrected image to complete the distortion correction.

[0015] Further, in step 1, the time-direction distorted image is obtained by the following method: a series of equally spaced laser pulses are output by a pulsed laser, which are expanded to uniformly cover the entire slit area of ​​the stripe camera, and recorded by the stripe camera under dynamic scanning conditions to obtain a pulse stripe image distorted along the scanning time axis.

[0016] Further, in step 1, the spatially distorted image is obtained by emitting pulsed light, which is then modulated by an equally spaced light intensity variation pattern to generate a pulsed light field distribution with equally spaced transmittance variations along the slit length direction of the stripe camera. This is recorded by the stripe camera under dynamic scanning conditions to obtain a stripe pattern image distorted along the spatial axis.

[0017] Furthermore, step 2 specifically involves:

[0018] The pulse stripes in the time-direction distortion image and the stripe pattern in the spatial-direction distortion image intersect in space, so that each intersection point carries both time distortion information and spatial distortion information, forming an intersection point grid image containing two-dimensional spatiotemporal distortion information.

[0019] Furthermore, in step 3, an image processing algorithm is used to extract the actual pixel coordinates of all intersection points in the intersection point grid image;

[0020] Step 3 specifically involves:

[0021] a. Filter and denoise the intersection grid image containing two-dimensional spatiotemporal distortion information to remove noise interference introduced during image acquisition;

[0022] b. Perform adaptive threshold binarization on the filtered and denoised intersection grid image to convert the image into a binary image and highlight the intersection area;

[0023] c. Perform morphological processing on the binarized intersection grid image to eliminate holes and burrs in the intersection region, so that the intersection region forms a complete connected region.

[0024] d. For the connected components of the morphologically processed intersection grid image, the skeleton extraction method or the sub-pixel centroid method is used to obtain the actual pixel coordinates of each intersection point.

[0025] 6. The method for correcting image distortion of a streak camera based on spatiotemporal intersection grid correction according to claim 1, characterized in that:

[0026] In step 4, the ideal grid coordinates are set according to the nominal pixel size and scanning range of the stripe camera; the mapping relationship is established through polynomial fitting or thin plate spline interpolation.

[0027] Furthermore, in step 4, the distortion correction matrix is ​​a two-dimensional spatial mapping matrix;

[0028] In step 5, the pixel-by-pixel coordinate transformation adopts the reverse mapping method, and the grayscale interpolation adopts bilinear interpolation, bicubic interpolation, or nearest neighbor interpolation.

[0029] Meanwhile, this invention also provides a stripe camera image distortion correction system based on spatiotemporal intersection grid correction, used to implement the above-mentioned stripe camera image distortion correction method based on spatiotemporal intersection grid correction, which is characterized by:

[0030] It includes a distortion measurement module, an image combination module, an intersection point extraction module, a correction matrix generation module, and an image correction module;

[0031] The distortion measurement module is used to acquire temporal and spatial distortion images from the streak camera under dynamic operating conditions.

[0032] The image combination module is used to superimpose and combine the temporal distortion image and the spatial distortion image to form an intersection grid image containing two-dimensional spatiotemporal distortion information.

[0033] The intersection point extraction module is used to extract the actual pixel coordinates of all intersection points in the intersection point grid image;

[0034] The correction matrix generation module is used to calculate the distortion correction matrix based on the preset mapping relationship between the ideal grid coordinates and the actual pixel coordinates;

[0035] The image correction module is used to apply the distortion correction matrix to all subsequent original images captured by the stripe camera, perform pixel-by-pixel coordinate transformation and grayscale interpolation, and output the corrected image.

[0036] Furthermore, the distortion measurement module includes a main signal source, a laser trigger source and a pulsed light source electrically connected to the main signal source, a pulsed laser electrically connected to the output end of the laser trigger source, an equally spaced light intensity variation pattern and an objective lens sequentially arranged on the emission optical path of the pulsed light source, a semi-transparent mirror, and a fringe camera; the semi-transparent mirror is located on the emission optical path of the pulsed laser and the output optical path of the objective lens, and is used to combine the two beams; the fringe camera is located on the transmission optical path of the combined beam;

[0037] The total signal source triggers the pulsed laser to output equally spaced pulse trains via the laser trigger source. After beam expansion, the pulse trains are incident on the semi-transparent mirror. Simultaneously, the total signal source triggers the pulsed light source to emit pulsed light. After being modulated by an equally spaced light intensity variation pattern, the pulsed light passes through the objective lens and enters the water into the semi-transparent mirror. The equally spaced pulse trains and the modulated pulsed light are combined by the semi-transparent mirror and enter the slit of the stripe camera. The stripe camera is used to record, under dynamic scanning conditions, pulse stripe images distorted along the scanning time axis and stripe pattern images distorted along the spatial axis.

[0038] Furthermore, the pulsed light source is a pulsed xenon lamp.

[0039] Compared with the prior art, the present invention has the following beneficial technical effects:

[0040] 1. The method for correcting image distortion of a stripe camera based on spatiotemporal intersection grid correction provided by the present invention combines the temporal distortion image with the spatial distortion image to form a unified spatiotemporal intersection grid in space. Each intersection point in the grid carries both temporal and spatial distortion information, realizing the organic fusion and complete representation of two-dimensional distortion information, effectively overcoming the problem of missing two-dimensional coupled distortion information caused by separate measurement and processing of the two types of distortion in traditional methods.

[0041] 2. The image distortion correction method for streak cameras based on spatiotemporal intersection grid correction provided by this invention includes step 3, which extracts the actual coordinates of the intersection points and establishes a mapping relationship between them and the coordinates of the ideal grid; and step 4, which generates a universal distortion correction matrix that can be directly applied to all subsequent images. This correction matrix is ​​calibrated once and reused repeatedly, eliminating the need to re-measure distortion and fit parameters in each experiment or image processing, greatly simplifying the correction process and significantly improving the efficiency of batch image processing. Compared with the traditional method that requires repeated calibration for each correction, this invention can save a lot of calibration time and computing resources in long-term, high-frequency streak camera usage scenarios.

[0042] 3. The image distortion correction method for a striped camera based on spatiotemporal intersection grid correction provided by this invention is constructed based on the distortion data of the striped camera under actual dynamic working conditions (i.e., the image obtained in step 1) in step 4. It can truly reflect the spatiotemporal distortion distribution of the camera under normal shooting conditions. The correction matrix performs pixel-by-pixel coordinate transformation and grayscale interpolation on the image, which fully considers the distortion differences in different local areas of the camera. It overcomes the shortcomings of traditional correction strategies based on overall average parameters that cannot accurately reflect local nonlinear distortion, and effectively improves the accuracy and reliability of distortion correction.

[0043] 4. The image distortion correction method for stripe cameras based on spatiotemporal intersection grid correction provided by this invention is not limited by the shape and size of the cathode of the stripe camera. It is applicable to both common rectangular photocathode stripe cameras and narrow stripe photocathode stripe cameras; it is applicable to visible light stripe cameras as well as X-ray and other band stripe cameras; regardless of the specific parameters such as scanning speed range and spatial resolution, this invention can achieve effective correction by constructing the corresponding spatiotemporal intersection grid, and has good universality.

[0044] 5. The image distortion correction method for streak cameras based on spatiotemporal intersection grid correction provided by this invention is implemented using image processing technology. It requires a simple calibration optical path structure and does not require any hardware modification to the streak camera itself. It can be directly integrated into the existing control software or data processing software of streak cameras. This feature enables the invention to be rapidly deployed and applied on existing equipment at extremely low cost, demonstrating good engineering practical value and promising prospects for widespread application.

[0045] 6. The stripe camera image distortion correction system based on spatiotemporal intersection grid correction provided by this invention acquires distorted images in two directions under dynamic working conditions through a temporal distortion measurement module and a spatial distortion measurement module, respectively. The image combination module then superimposes and fuses these two images into a two-dimensional intersection grid that simultaneously carries both temporal and spatial distortion information. This overcomes the defect of traditional methods where the two types of distortion are processed separately, resulting in the loss of two-dimensional coupled distortion information. The system extracts the coordinates of the grid intersection points through an intersection point extraction module, and establishes a mapping relationship between the actual coordinates and the ideal grid coordinates through a correction matrix generation module, generating a universal distortion correction matrix. A single calibration allows the image correction module to perform batch correction on all subsequent images without repeated calibration, significantly improving correction efficiency. Simultaneously, the correction matrix is ​​constructed based on distortion data under actual dynamic working conditions, performing pixel-by-pixel coordinate transformation and grayscale interpolation on the image, fully considering the distortion differences in different local areas of the camera, and effectively improving correction accuracy.

[0046] 7. The image distortion correction system for a streak camera based on spatiotemporal cross-point grid correction provided by this invention uses a total signal source, a laser trigger source, and a pulsed laser to generate and incident equally spaced pulse trains in the temporal distortion measurement module. The spatial distortion measurement module uses a total signal source, a pulsed light source, equally spaced light intensity variation patterns, and an objective lens to generate and image a spatially modulated beam. The two beams are combined by a semi-transparent and semi-reflective mirror and then enter the slit of the streak camera together. The two modules share the same total signal source to ensure strict time synchronization. The entire system has a simple optical path structure, requires no hardware modification to the streak camera itself, and can be flexibly integrated into the control software of existing streak cameras. It can be quickly deployed and applied on the basis of existing equipment at extremely low cost. It is suitable for streak cameras with various cathode shapes and wavelengths, and has good engineering practical value and promotion prospects. Attached Figure Description

[0047] Figure 1 This is a flowchart of the stripe camera image distortion correction method based on spatiotemporal intersection grid correction according to the present invention.

[0048] Figure 2 This is a schematic diagram of the temporal distortion image in step 1 of an embodiment of the stripe camera image distortion correction method based on spatiotemporal intersection grid correction of the present invention;

[0049] Figure 3 This is a schematic diagram of the spatial orientation distortion image in step 1 of an embodiment of the stripe camera image distortion correction method based on spatiotemporal intersection grid correction of the present invention;

[0050] Figure 4 This is a schematic diagram of the cross-point grid image containing two-dimensional spatiotemporal distortion information in step 3 of an embodiment of the stripe camera image distortion correction method based on spatiotemporal cross-point grid correction of the present invention.

[0051] Figure 5 This is a schematic diagram illustrating the mapping relationship between the ideal grid coordinates and the actual pixel coordinates in step 4 of an embodiment of the stripe camera image distortion correction method based on spatiotemporal intersection grid correction of the present invention.

[0052] Figure 6 This is a schematic diagram of the corrected image in step 5 of an embodiment of the stripe camera image distortion correction method based on spatiotemporal intersection grid correction of the present invention;

[0053] Figure 7 This is a schematic diagram of the distortion measurement module in an embodiment of the stripe camera image distortion correction system based on spatiotemporal intersection grid correction of the present invention.

[0054] The annotations in the attached figures are explained as follows:

[0055] 1-Main signal source, 2-Laser trigger source, 3-Laser, 4-Pulse light source, 5-Equally spaced light intensity variation pattern, 6-Objective lens, 7-Semi-transparent and semi-reflective mirror, 8-Stripe camera. Detailed Implementation

[0056] To make the objectives, advantages, and features of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that these embodiments are merely used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] This embodiment first provides a stripe camera image distortion correction system based on spatiotemporal intersection grid correction, including a distortion measurement module, an image combination module, an intersection extraction module, a correction matrix generation module, and an image correction module.

[0058] The functions of each module are as follows: The distortion measurement module is used to acquire temporal and spatial distortion images from the streak camera under dynamic working conditions; the image combination module is used to superimpose and combine the temporal and spatial distortion images to form an intersection grid image containing two-dimensional spatiotemporal distortion information; the intersection extraction module is used to extract the actual pixel coordinates of all intersections in the intersection grid image; the correction matrix generation module is used to calculate the distortion correction matrix based on the preset mapping relationship between the ideal grid coordinates and the actual pixel coordinates; the image correction module is used to apply the distortion correction matrix to all subsequent original images captured by the streak camera, perform pixel-by-pixel coordinate transformation and grayscale interpolation, and output the corrected image.

[0059] The system described above acquires distorted images in two directions under dynamic working conditions through a distortion measurement module. The image combination module then superimposes and fuses these two images, overcoming the shortcomings of traditional methods that separate the processing of two types of distortion, resulting in the loss of two-dimensional coupled distortion information. The system extracts the coordinates of grid intersection points through an intersection point extraction module, and a universal distortion correction matrix is ​​generated by a correction matrix generation module. A single calibration allows the image correction module to perform batch correction on all subsequent images without repeated calibration, significantly improving correction efficiency. At the same time, the distortion correction matrix is ​​constructed based on distortion data under actual dynamic working conditions, performing pixel-by-pixel coordinate transformation and grayscale interpolation on the image. This fully considers the distortion differences in different local areas of the streak camera, effectively improving correction accuracy.

[0060] Specifically, see Figure 7 The distortion measurement module includes a main signal source 1, a laser trigger source 2 and a pulsed light source 4 electrically connected to the main signal source 1, a pulsed laser 3 electrically connected to the output of the laser trigger source 2, an equally spaced light intensity variation pattern 5 and an objective lens 6 sequentially arranged on the emission optical path of the pulsed light source 4, a semi-transparent mirror 7, and a streak camera 8. The semi-transparent mirror 7 is located on the emission optical path of the pulsed laser 3 and the output optical path of the objective lens 6, and is used to combine the two beams. The streak camera 8 is located on the transmission optical path of the combined beam. The main signal source 1 is triggered by the laser. Source 2 triggers pulsed laser 3 to output equally spaced pulse trains, which are then beam-expanded and incident on the semi-transparent mirror 7. At the same time, the total signal source 1 triggers pulsed light source 4 to emit pulsed light. After being modulated by equally spaced light intensity change pattern 5, the pulsed light passes through objective lens 6 and enters the water to the semi-transparent mirror 7. The equally spaced pulse train and the modulated pulsed light are combined by the semi-transparent mirror 7 and enter the slit of the streak camera 8. The streak camera 8 is used to record the pulsed stripe image distorted along the scanning time axis and the stripe pattern image distorted along the spatial axis under dynamic scanning conditions.

[0061] The optical path structure of this system is simple. The temporal distortion measurement module uses a total signal source 1, a laser trigger source 2, and a pulsed laser 3 to generate and incident equally spaced pulse trains. The spatial distortion measurement module uses a total signal source 1, a pulsed light source 4, equally spaced light intensity variation patterns 5, and an objective lens 6 to generate and image spatially modulated beams. The two beams are combined by a semi-transparent mirror 7 and then enter the slit of the streak camera 8. The two modules share the same total signal source to ensure strict time synchronization. No hardware modification to the streak camera itself is required. It can be flexibly integrated into the control software of existing streak cameras and deployed and applied quickly at extremely low cost based on existing equipment.

[0062] Next, this embodiment utilizes the above system, taking a streak camera 8 with a scan range of 1000ns as an example, to provide a streak camera image distortion correction method based on spatiotemporal intersection grid correction. The flowchart is shown below. Figure 1 This includes the following steps:

[0063] Step 1: Under dynamic operating conditions, a series of equally spaced (40ns interval) laser pulses are output from pulsed laser 3. After beam expansion, these pulses uniformly cover the entire slit area of ​​the streak camera. The streak camera 8 records the pulsed stripe image under dynamic scanning conditions, resulting in a pulsed stripe image distorted along the scanning time axis. See [link to relevant documentation]. Figure 2 A pulsed xenon lamp was used as the pulsed light source 4. Black lines were printed on paper at 5mm intervals to form equally spaced light-transmitting slits, creating equally spaced light intensity variation patterns 5. A pulsed light field distribution with equally spaced transmittance variations was generated along the slit length direction of the stripe camera 8. The stripe camera 8 recorded the image under dynamic scanning conditions, obtaining a stripe pattern image distorted along the spatial axis. See [link to documentation]. Figure 3 Both temporal and spatial distortion images were acquired under dynamic operating conditions, enabling a true record of the distortion characteristics of the streak camera during actual scanning, thus providing accurate raw data for subsequent calculation of the correction matrix.

[0064] Step 2: Spatially intersect the pulse stripes in the temporal distortion image with the stripe pattern in the spatial distortion image, so that each intersection point carries both temporal and spatial distortion information, forming an intersection point grid image containing two-dimensional spatiotemporal distortion information. (See [link to relevant documentation]). Figure 4 This step combines the temporal distortion image with the spatial distortion image, creating a unified spatiotemporal intersection grid of the two originally independently measured distortion information. Each intersection point in this grid carries both temporal and spatial distortion information, achieving the organic fusion and complete representation of two-dimensional distortion information. This effectively overcomes the problem of missing two-dimensional coupled distortion information caused by the separate measurement and processing of the two types of distortion in traditional methods.

[0065] Step 3: Extract the actual pixel coordinates of all intersection points in the intersection point grid image using an image processing algorithm; the algorithm includes the following steps:

[0066] a. Filter and denoise the spatiotemporal intersection grid image to remove noise interference introduced during image acquisition;

[0067] b. Perform adaptive threshold binarization on the filtered and denoised image to convert the image into a binary image and highlight the intersection area;

[0068] c. Perform morphological processing on the binarized image to eliminate holes and burrs in the intersection area, so that the intersection area forms a complete connected region;

[0069] d. For the morphologically processed connected components, the sub-pixel centroid method is used to obtain the actual coordinates of each intersection point.

[0070] This step extracts the actual coordinates of the intersection points and establishes a mapping relationship between them and the ideal grid coordinates, providing a data foundation for the subsequent generation of a universal distortion correction matrix. This distortion correction matrix can be calibrated once and reused repeatedly, eliminating the need to re-measure distortion and fit parameters for each experiment or each image processing, greatly simplifying the correction process and significantly improving the efficiency of batch image processing. Compared to the traditional method that requires repeated calibration for each correction, this method can save a significant amount of calibration time and computational resources in long-term, high-frequency streak camera usage scenarios.

[0071] Step 4: Calculate the distortion correction matrix based on the preset mapping relationship between ideal grid coordinates and actual pixel coordinates. The ideal grid coordinates are set according to the nominal pixel size and scanning range of the stripe camera; in this embodiment, the grid size is set to 40 ns (time) × 5 mm (space). The mapping relationship is established through polynomial fitting. See the schematic diagram of the mapping relationship between the ideal grid coordinates and the actual pixel coordinates. Figure 5 ;

[0072] Specifically, the polynomial fitting adopts a mapping model of the following form:

[0073]

[0074] in, These are the actual coordinates of the intersection. For the corresponding ideal grid coordinates, , The mapping coefficients to be fitted are obtained by solving the least squares method, and the obtained mapping coefficients are stored as a distortion correction matrix.

[0075] Step 5: Using the distortion correction matrix (a two-dimensional spatial mapping matrix in this embodiment), perform pixel-by-pixel coordinate transformation and grayscale interpolation on all subsequent original images captured by the streak camera, and output the corrected image. See [link to documentation]. Figure 6 Distortion correction is completed; the pixel-by-pixel coordinate transformation adopts the inverse mapping method, and the grayscale interpolation adopts bilinear interpolation, bicubic interpolation or nearest neighbor interpolation.

[0076] The generated distortion correction matrix performs pixel-by-pixel coordinate transformation and grayscale interpolation on the image, fully considering the distortion differences in different local regions of the camera. This overcomes the shortcomings of traditional correction strategies based on overall average parameters, which cannot accurately reflect local nonlinear distortions, effectively improving the accuracy and reliability of distortion correction. This method is entirely based on image processing technology, requiring a simple calibration optical path structure. It requires no hardware modification to the streak camera itself and can be directly integrated into the control or data processing software of existing streak cameras. It can be rapidly deployed and applied on existing equipment at extremely low cost, demonstrating good engineering practical value and promising prospects for widespread adoption. Furthermore, this method is not limited by the shape and size of the streak camera's cathode; it is applicable to both common rectangular photocathode streak cameras and narrow stripe photocathode streak cameras; it is applicable to visible light streak cameras as well as X-ray and other band streak cameras, exhibiting good versatility.

[0077] It should be noted that the specific parameters described in the above embodiments (such as a scan range of 1000 ns, a pulse interval of 40 ns, and a pattern interval of 5 mm) are illustrative and do not constitute a limitation of the present invention. In practical applications, parameters such as the scan range, pulse interval, and pattern interval can be flexibly adjusted according to the nominal parameters of different models of stripe cameras and actual measurement requirements. For example, for a stripe camera with a scan range of 500 ns, the pulse interval can be set to 20 ns accordingly; for applications requiring higher spatial resolution, the interval of the transmittance pattern can be reduced to obtain denser spatial sampling points.

[0078] Furthermore, the establishment of the mapping relationship is not limited to quadratic polynomial fitting; thin-plate spline interpolation, radial basis function interpolation, or other applicable image registration methods can also be used, all of which can achieve the technical effects of the present invention. In addition to bilinear interpolation, the grayscale interpolation method can also use bicubic interpolation or nearest-neighbor interpolation; those skilled in the art can choose according to the actual needs of accuracy and computational efficiency.

[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for correcting image distortion in a streak camera based on spatiotemporal intersection grid correction, characterized in that, Includes the following steps: Step 1: Under dynamic working conditions, acquire the temporal distortion image and spatial distortion image of the streak camera respectively; Step 2: Superimpose and combine the temporal distortion image and the spatial distortion image to form an intersection grid image containing two-dimensional spatiotemporal distortion information; Step 3: Extract the actual pixel coordinates of all intersection points in the intersection point grid image; Step 4: Calculate the distortion correction matrix based on the preset mapping relationship between the ideal grid coordinates and the actual pixel coordinates; Step 5: Use the distortion correction matrix to perform pixel-by-pixel coordinate transformation and grayscale interpolation on all subsequent original images captured by the stripe camera, and output the corrected image to complete the distortion correction.

2. The method for correcting image distortion of a streak camera based on spatiotemporal intersection grid correction according to claim 1, characterized in that: In step 1, the time-direction distorted image is obtained by the following method: a series of equally spaced laser pulses are output by a pulsed laser, which are expanded to uniformly cover the entire slit area of ​​the stripe camera, and recorded by the stripe camera under dynamic scanning conditions to obtain a pulse stripe image distorted along the scanning time axis.

3. The method for correcting streak camera image distortion based on spatiotemporal intersection grid correction according to claim 1 or 2, characterized in that: In step 1, the spatially distorted image is obtained by emitting pulsed light, which is then modulated by an equally spaced light intensity variation pattern to generate a pulsed light field distribution with equally spaced transmittance variations along the slit length direction of the stripe camera. The stripe camera records the image under dynamic scanning conditions to obtain a stripe pattern image distorted along the spatial axis.

4. The method for correcting image distortion of a streak camera based on spatiotemporal intersection grid correction according to claim 1, characterized in that, Step 2 is as follows: The pulse stripes in the time-direction distortion image and the stripe pattern in the spatial-direction distortion image intersect in space, so that each intersection point carries both time distortion information and spatial distortion information, forming an intersection point grid image containing two-dimensional spatiotemporal distortion information.

5. The method for correcting image distortion of a streak camera based on spatiotemporal intersection grid correction according to claim 1, characterized in that: In step 3, an image processing algorithm is used to extract the actual pixel coordinates of all intersection points in the intersection point grid image; Step 3 specifically involves: a. Filter and denoise the intersection grid image containing two-dimensional spatiotemporal distortion information to remove noise interference introduced during image acquisition; b. Perform adaptive threshold binarization on the filtered and denoised intersection grid image to convert the image into a binary image and highlight the intersection area; c. Perform morphological processing on the binarized intersection grid image to eliminate holes and burrs in the intersection region, so that the intersection region forms a complete connected region. d. For the connected components of the morphologically processed intersection grid image, the skeleton extraction method or the sub-pixel centroid method is used to obtain the actual pixel coordinates of each intersection point.

6. The method for correcting image distortion of a streak camera based on spatiotemporal intersection grid correction according to claim 1, characterized in that: In step 4, the ideal grid coordinates are set according to the nominal pixel size and scanning range of the stripe camera; the mapping relationship is established through polynomial fitting or thin plate spline interpolation.

7. The method for correcting image distortion of a streak camera based on spatiotemporal intersection grid correction according to claim 1, characterized in that: In step 4, the distortion correction matrix is ​​a two-dimensional spatial mapping matrix; In step 5, the pixel-by-pixel coordinate transformation adopts the reverse mapping method, and the grayscale interpolation adopts bilinear interpolation, bicubic interpolation, or nearest neighbor interpolation.

8. A streak camera image distortion correction system based on spatiotemporal intersection grid correction, used to implement the streak camera image distortion correction method based on spatiotemporal intersection grid correction as described in any one of claims 1 to 7, characterized in that: It includes a distortion measurement module, an image combination module, an intersection point extraction module, a correction matrix generation module, and an image correction module; The distortion measurement module is used to acquire temporal and spatial distortion images from the streak camera under dynamic operating conditions. The image combination module is used to superimpose and combine the temporal distortion image and the spatial distortion image to form an intersection grid image containing two-dimensional spatiotemporal distortion information. The intersection point extraction module is used to extract the actual pixel coordinates of all intersection points in the intersection point grid image; The correction matrix generation module is used to calculate the distortion correction matrix based on the preset mapping relationship between the ideal grid coordinates and the actual pixel coordinates; The image correction module is used to apply the distortion correction matrix to all subsequent original images captured by the stripe camera, perform pixel-by-pixel coordinate transformation and grayscale interpolation, and output the corrected image.

9. The stripe camera image distortion correction system based on spatiotemporal intersection grid correction according to claim 8, characterized in that: The distortion measurement module includes a main signal source (1), a laser trigger source (2) and a pulsed light source (4) electrically connected to the main signal source (1), a pulsed laser (3) electrically connected to the output end of the laser trigger source (2), an equally spaced light intensity variation pattern (5) and an objective lens (6) arranged sequentially on the emission optical path of the pulsed light source (4), a semi-transparent mirror (7), and a stripe camera (8); the semi-transparent mirror (7) is located on the emission optical path of the pulsed laser (3) and the output optical path of the objective lens (6) and is used to combine the two beams; the stripe camera (8) is located on the transmission optical path of the combined beam; The total signal source (1) triggers the pulsed laser (3) to output an equally spaced pulse train through the laser trigger source (2), which is then beam-expanded and incident on the semi-transparent mirror (7). At the same time, the total signal source (1) triggers the pulsed light source (4) to emit pulsed light. After being modulated by the equally spaced light intensity change pattern (5), the pulsed light enters the water through the objective lens (6) and then enters the semi-transparent mirror (7). The equally spaced pulse train and the modulated pulsed light are combined through the semi-transparent mirror (7) and then enter the slit of the stripe camera (8). The stripe camera (8) is used to record the pulse stripe image distorted along the scanning time axis and the stripe pattern image distorted along the spatial axis under dynamic scanning conditions.

10. The stripe camera image distortion correction system based on spatiotemporal intersection grid correction according to claim 9, characterized in that: The pulse light source (4) is a pulsed xenon lamp.

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