A method and electronic device for intensity correction of interferometric images

Abstract

This invention discloses a method and electronic device for intensity correction of interferometric images, relating to the field of phase detection technology for mosaic telescopes. Based on the grayscale values ​​of each pixel in a sample interferometric image dataset, the values ​​of parameters representing the dispersion of grayscale values ​​and the reference level of grayscale values ​​for each pixel are determined, i.e., correction coefficients. After acquiring the actual interferometric image, the grayscale values ​​of the corresponding pixels in the actual interferometric image are corrected using the correction coefficients to obtain the target interferometric image, thus achieving intensity correction of the interferometric image. Furthermore, determining the correction coefficients based on the sample interferometric image dataset eliminates the need for a built-in calibration device to block one path, consistent with real interferometric measurement scenarios, and improves the measurement accuracy of the synthesized phase. Moreover, after obtaining the correction coefficients for each pixel, correction is performed pixel-by-pixel based on the correction coefficients of each pixel, improving the accuracy of interferometric image correction and further enhancing the measurement accuracy of the synthesized phase.

Description

Technical Field

[0001] This invention relates to the field of phase detection technology for spliced ​​telescopes, and in particular to a method and electronic device for light intensity correction of interferometric images. Background Technology

[0002] Modular telescopes, with their advantages of low fabrication difficulty for individual sub-mirrors, flexible construction, and easy expansion, have become one of the important directions for the development of future large-aperture telescopes. To achieve a resolution similar to that of a single primary mirror, modular telescopes need to accurately detect the common-phase error between each sub-mirror to guide the assembly and adjustment process. Currently, there are various methods for detecting common-phase error. Among them, multi-wavelength synchronous phase-shift interferometry not only boasts high accuracy, simple optical path, and short detection time, but also effectively overcomes the limitation of small dynamic range, thus solving the common-phase detection problem well. However, for multi-wavelength synchronous phase-shift interferometry, the inconsistent light intensity of images at different wavelengths and phase shifts poses significant challenges to the processing of interference images. Improper processing can result in ripple errors in the measurement results that correspond to the direction of the interference fringes. These ripple errors are further amplified during the synthesis phase calculation due to the error amplification effect, severely impacting the final measurement accuracy.

[0003] In conventional intensity consistency correction methods, multi-wavelength synchronous phase-shift interferometry devices incorporate a dedicated calibration unit. The reference or measurement beam is blocked, and images of the corresponding light spots are acquired separately. Intensity consistency correction is then performed on the interferometric image based on the average intensity of the light spots. This method requires a dedicated calibration unit, and the calibration scenario differs from the actual measurement scenario. This often results in residual ripple errors after correction and phase-shift error correction, leading to a decrease in the measurement accuracy of the synthesized phase.

[0004] Therefore, how to effectively correct the light intensity of phase-shifted images to improve the measurement accuracy of the synthesized phase is a technical problem that urgently needs to be solved by those in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a light intensity correction method and electronic device for interferometric images, in order to solve the technical problem that related light intensity consistency correction methods require a built-in dedicated calibration device, and the calibration scenario is inconsistent with the actual measurement scenario, which often results in residual ripple error after correction and phase shift error correction, leading to a decrease in the measurement accuracy of the synthesized phase.

[0006] To solve the above-mentioned technical problems, the present invention provides a method for intensity correction of interferometric images, comprising:

[0007] Acquire a dataset of sample interference images collected by the interferometer under a light source of the target wavelength, and obtain the gray value of each pixel in the sample interference image dataset;

[0008] Based on the gray values ​​of each pixel, determine the values ​​of the parameters that characterize the degree of dispersion of gray values ​​and the parameters that characterize the baseline level of gray values ​​for each pixel;

[0009] Obtain the actual interference image obtained when using a light source with the target wavelength for interferometry, and obtain the gray value of each pixel in the actual interference image;

[0010] Based on the values ​​of the parameters representing the degree of grayscale dispersion corresponding to each pixel and the values ​​of the parameters representing the baseline level of grayscale values, the grayscale values ​​of the corresponding pixels in the actual interference image are corrected to obtain the target interference image.

[0011] For example, acquiring a dataset of sample interferometric images collected by an interferometer under a light source of the target wavelength includes:

[0012] With the intensity of the light source at the target wavelength controlled to the target intensity and the exposure time controlled to the target exposure time, the movement of the moving device connected to the splicing mirror is controlled to drive the splicing mirror to move along the optical axis.

[0013] During the movement of the splicing mirror, the interferometer is controlled to perform multiple imaging operations on the splicing mirror to obtain the sample interference image dataset.

[0014] For example, before determining the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the reference level of gray values ​​for each pixel based on the gray values ​​of each pixel, the method further includes:

[0015] Obtain the average grayscale value of all pixels within the target area where the preset pixel is located; wherein, the preset pixel is any pixel in the sample interference image dataset;

[0016] Obtain the grayscale difference between the grayscale value of the preset pixel and the grayscale mean value;

[0017] If the grayscale difference is detected to be greater than the threshold, the preset pixel is marked as an abnormal pixel, and the grayscale mean is used as the grayscale value of the preset pixel to obtain a new sample interference image dataset.

[0018] The determination of the values ​​of the parameters representing the degree of dispersion of gray values ​​for each pixel and the parameters representing the reference level of gray values ​​based on the gray values ​​of each pixel includes:

[0019] Based on the gray values ​​of each pixel in the new sample interferometric image dataset, the values ​​of the parameters representing the degree of dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined.

[0020] For example, the determination of the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel based on the gray values ​​of each pixel in the new sample interferometric image dataset includes:

[0021] The stitching mirror region in the new sample interferometric image dataset is determined based on the gray values ​​of each pixel in the new sample interferometric image dataset.

[0022] Obtain the image dataset corresponding to the stitching mirror region in the new sample interference image dataset;

[0023] Based on the gray values ​​of each pixel in the image dataset corresponding to the stitching mirror region, the values ​​of the parameters representing the degree of dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined.

[0024] For example, determining the stitching mirror region in the new sample interferometric image dataset based on the gray values ​​of each pixel in the new sample interferometric image dataset includes:

[0025] Obtain a reference image from the new sample interferometric image dataset, as well as the remaining images other than the reference image; wherein the reference image is any image in the new sample interferometric image dataset;

[0026] Obtain the absolute value of the difference between the gray value of each pixel in the remaining image and the gray value of the corresponding pixel in the reference image;

[0027] Obtain the sum of the absolute values ​​of the differences corresponding to the same pixel in the remaining image, and determine the difference image based on the sum of the absolute values ​​of the differences corresponding to all pixels;

[0028] The difference image is binarized and morphological closing is performed to obtain the splicing mirror template image;

[0029] Using the stitching mirror template image, template matching is performed on all images in the new sample interferometric image dataset to obtain the stitching mirror region in the new sample interferometric image dataset.

[0030] For example, based on the gray values ​​of each pixel in the image dataset corresponding to the stitching mirror region, the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined as follows:

[0031] In the image dataset corresponding to the splicing mirror region, curve fitting is performed on the gray value of the same pixel, and the maximum and minimum gray values ​​at the pixel position are determined based on the fitted curves.

[0032] A global bright map is created based on the maximum gray value corresponding to all pixel positions, and a global dark map is created based on the minimum gray value corresponding to all pixel positions.

[0033] Obtain the maximum and minimum grayscale values ​​at the same pixel location in the global bright image and the global dark image;

[0034] The value of the parameter representing the degree of dispersion of gray values ​​for each pixel is determined by obtaining half the difference between the maximum and minimum gray values ​​at the same pixel location.

[0035] The value of the parameter representing the grayscale baseline level corresponding to each pixel is determined by obtaining half of the sum of the maximum and minimum grayscale values ​​at the same pixel location.

[0036] For example, before obtaining the grayscale value of each pixel in the actual interference image, the method further includes:

[0037] Abnormal pixel detection is performed on the actual interference image to obtain a new actual interference image;

[0038] The image data corresponding to the actual splicing mirror area is determined based on the gray values ​​of each pixel in the new actual interference image.

[0039] Obtaining the grayscale value of each pixel in the actual interference image includes:

[0040] Obtain the grayscale value of each pixel in the image data corresponding to the actual splicing mirror area.

[0041] For example, based on the values ​​of parameters representing the dispersion of gray values ​​corresponding to each pixel and the values ​​of parameters representing the reference level of gray values, the gray values ​​of corresponding pixels in the actual interferometric image are corrected to obtain the target interferometric image, including:

[0042] Obtain the first difference between the gray value of the target pixel in the actual interferometric image and the value of a parameter characterizing the gray value reference level; wherein, the target pixel is any pixel in the actual interferometric image;

[0043] Obtain the ratio between the first difference and the value of the parameter characterizing the degree of dispersion of gray values, and use the ratio as the corrected gray value corresponding to the target pixel;

[0044] The target interference image is obtained based on the corrected gray values ​​corresponding to each target pixel.

[0045] For example, after obtaining the target interferometric image, the process also includes:

[0046] The steps include controlling the light source to operate at the new target wavelength, returning to the step of acquiring the sample interference image dataset collected by the interferometer under the target wavelength light source, and acquiring the grayscale value of each pixel in the sample interference image dataset.

[0047] To address the aforementioned technical problems, the present invention also provides an electronic device, comprising:

[0048] Memory, used to store computer programs;

[0049] A processor is used to implement the steps of the above-described method for correcting the light intensity of an interferometric image when executing the computer program.

[0050] The method for intensity correction of interferometric images provided by this invention first obtains the grayscale value of each pixel in a sample interferometric image dataset acquired by an interferometer under a light source of the target wavelength. Then, based on the grayscale value of each pixel, the values ​​of a parameter representing the degree of dispersion of the grayscale value and a parameter representing the reference level of the grayscale value are determined for each pixel. After obtaining the actual interferometric image obtained when using a light source of the target wavelength for interferometry, the grayscale value of each pixel in the actual interferometric image is obtained. Finally, using the values ​​of the parameters representing the degree of dispersion of the grayscale value and the reference level of the grayscale value for each pixel, the grayscale value of the corresponding pixel in the actual interferometric image is corrected, thus achieving the correction of the grayscale value of each pixel in the actual interferometric image and obtaining the target interferometric image. In other words, this method achieves the correction of the intensity of the interferometric image. Furthermore, compared to the method of using a built-in dedicated calibration device to block one path and then using the light spot corresponding to the unblocked optical path for light intensity correction, in this invention, the values ​​of the parameters representing the degree of grayscale dispersion and the parameters representing the grayscale reference level (i.e., correction coefficients) corresponding to each pixel are determined based on the grayscale values ​​of each pixel in the sample interferometric image dataset. This eliminates the need for a built-in calibration device to block one path, aligning with the actual interferometric measurement scenario. This avoids the problem of residual ripple error after correction and phase shift error correction when using the correction coefficients to correct the actual interferometric image, thus improving the measurement accuracy of the synthesized phase. Moreover, after obtaining the correction coefficients for each pixel, correction is performed pixel by pixel based on the correction coefficients of each pixel, improving the accuracy of interferometric image correction and further enhancing the measurement accuracy of the synthesized phase.

[0051] In addition, the present invention also provides an electronic device that has the same or corresponding technical features as the above-mentioned method for correcting the light intensity of interference images, and achieves the same effect. Attached Figure Description

[0052] To more clearly illustrate the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1This is a schematic diagram of a dual-wavelength synchronous phase-shift interferometry device provided in an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram illustrating the imaging principle of a micro-polarization camera.

[0055] Figure 3 A flowchart of an intensity correction method for an interferometric image provided in an embodiment of the present invention;

[0056] Figure 4 A flowchart illustrating an overall method for intensity correction of an interferometric image provided in an embodiment of the present invention;

[0057] Figure 5 A flowchart illustrating a method for obtaining a sample interferometric image dataset for calibration, provided in an embodiment of the present invention;

[0058] Figure 6 This is a flowchart of a method for performing splicing mirror target detection on a new sample interferometric image dataset I2 to obtain an effective region dataset I3, provided by an embodiment of the present invention;

[0059] Figure 7 This is a flowchart illustrating a method for extracting global bright and global dark maps from a dataset I3 of an effective region, as provided in an embodiment of the present invention.

[0060] Figure 8 This is a structural diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0061] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0062] The core of this invention is to provide a light intensity correction method and electronic device for interferometric images, in order to solve the technical problem that related light intensity consistency correction methods require a built-in dedicated calibration device, and the calibration scenario is inconsistent with the actual measurement scenario, which often leaves residual ripple error after correction and phase shift error correction, resulting in a decrease in the measurement accuracy of the synthesized phase.

[0063] Modular telescopes, with their advantages of low fabrication difficulty for individual sub-mirrors, flexible construction, and easy expansion, have become one of the important directions for the development of future large-aperture telescopes. To achieve a resolution close to that of a single primary mirror, modular telescopes need to accurately detect the common-phase error between each sub-mirror to guide the assembly and adjustment process. Currently, there are various methods for detecting common-phase error. Among them, the multi-wavelength synchronous phase-shift interferometry method not only has high precision, simple optical path, and short detection time, but also effectively overcomes the defect of small dynamic range, and can well solve the common-phase detection problem. This method uses multiple light sources of different wavelengths to expand the system's dynamic range and typically employs a micro-polarization camera to simultaneously achieve four-step phase-shift imaging. Figure 1 This is a schematic diagram of a dual-wavelength synchronous phase-shift interferometry device provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the system includes a first single-mode light source 1, a second single-mode light source 2, an adjustable filter 3, a first polarizing beam splitter 4, a beam expander and collimator system 5, a half-wave plate 6, a second polarizing beam splitter 7, a quarter-wave plate 8, a plane reference mirror 9, a plane standard mirror 10, a mirror under test 11, an imaging lens group 12, a pinhole 13, and a micro-polarization camera 14. The wavelength of the first single-mode light source 1 is λ1, and the wavelength of the second single-mode light source 2 is λ2. Figure 2 This is a schematic diagram illustrating the imaging principle of a micro-polarization camera. First, a pixel-level polarization mask is integrated onto the surface of the micro-polarization camera 14. This mask uses pixel units with four different polarization responses (A, B, C, and D) arranged in a fixed 2×2 array for repeated operation, achieving pixel-level parallel modulation of the incident light's polarization state. Then, the micro-polarization camera 14 acquires a single-frame interferometric image containing polarization modulation information. Next, an image analysis algorithm separates and reconstructs the grayscale information of different polarization channels at the same spatial location, ultimately obtaining four phase-shifted interferometric fringe patterns with a fixed phase difference. This provides fundamental data for subsequent phase extraction and interferometric measurement analysis.

[0064] For multi-wavelength synchronous phase-shift interferometry, the inconsistency in light intensity at different wavelengths and phase shifts poses significant challenges to the processing of the interferometric images. Improper processing can result in ripple errors in the measurement results that mirror the direction of the interference fringes. These ripple errors are further amplified during the synthesis phase calculation due to the error amplification effect, severely impacting the final measurement accuracy.

[0065] Conventional methods for achieving intensity consistency correction by blocking the reference and measurement lights require a dedicated calibration device, and the calibration scenario differs from the actual measurement scenario. Furthermore, ripple errors often remain after intensity consistency and phase shift error correction, leading to a decrease in the phase measurement accuracy of the synthesized wavelength. This invention proposes an intensity correction method based on interferometric images. This method eliminates the need for a dedicated calibration device and corresponding calibration process, enabling pixel-by-pixel intensity correction between images of different wavelengths and phase shifts. This effectively removes the influence of ripple errors caused by inconsistencies in intensity between images of different wavelengths and phase shifts on the measurement results during synthesized phase calculation.

[0066] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Figure 3 A flowchart of an intensity correction method for an interferometric image provided in an embodiment of the present invention is shown below. Figure 3 As shown, the method includes:

[0067] S1: Obtain the sample interference image dataset acquired by the interferometer under the light source of the target wavelength, and obtain the gray value of each pixel in the sample interference image dataset;

[0068] S2: Based on the gray values ​​of each pixel, determine the values ​​of the parameters that characterize the degree of gray value dispersion and the parameters that characterize the gray value baseline level for each pixel;

[0069] S3: Obtain the actual interference image obtained when using a light source with the target wavelength for interferometry, and obtain the gray value of each pixel in the actual interference image;

[0070] S4: Based on the values ​​of the parameters representing the degree of grayscale dispersion corresponding to each pixel and the values ​​of the parameters representing the grayscale reference level, the grayscale values ​​of the corresponding pixels in the actual interference image are corrected to obtain the target interference image.

[0071] A multi-wavelength interferometer includes light sources with two or more measurement wavelengths. To obtain a sample interferometric image dataset, in one possible implementation, obtaining the sample interferometric image dataset acquired by the interferometer under a light source at the target wavelength includes:

[0072] With the intensity of the light source at the target wavelength set to the target intensity and the exposure time set to the target exposure time, the movement of the moving device connected to the splicing mirror is controlled to drive the splicing mirror to move along the optical axis.

[0073] During the movement of the splicing mirror, the interferometer is controlled to perform multiple imaging operations on the splicing mirror to obtain a sample interference image dataset.

[0074] The target wavelength is not limited and is determined based on actual conditions. The intensity of the light source controlling the target wavelength is the target intensity, and the exposure time is the target exposure time, to achieve optimal contrast in the interference fringe imaging and ensure unsaturated pixel gray values. The shape of the sub-mirrors in the mosaic telescope is not strictly constrained and can be conventionally circular or hexagonal. Each sub-mirror has a connected six-legged device on its back for displacement and angle adjustment, with an adjustment accuracy at the nanometer level. By controlling all the six-legged devices connected to the mosaic mirrors, each mosaic mirror moves at a uniform speed along the optical axis.

[0075] After obtaining the sample interferometric image dataset acquired by the interferometer under a light source of the target wavelength, the grayscale value of each pixel in the sample interferometric image dataset is further obtained. Specifically, the image is read in as a digital matrix, with each pixel corresponding to an element in the matrix. The value of this element is the grayscale value of the corresponding pixel. By traversing the elements in each row and column of the matrix, the grayscale value of each pixel in the image can be obtained sequentially.

[0076] Based on the obtained gray values ​​of each pixel, the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined. The parameters representing the dispersion of gray values ​​can be range, variance, etc. The parameters representing the baseline level of gray values ​​can be mean, median, etc. The following explanation uses half the range as an example and the mean as the baseline level of gray values ​​to illustrate the process of determining the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel.

[0077] From all sample interferometric image datasets, obtain the maximum and minimum gray values ​​of the same pixel, and obtain the maximum gray value of that pixel. minimum gray value Half of the difference is used as a parameter representing the degree of dispersion of the gray value corresponding to that pixel. The value of . Calculate the parameter characterizing the dispersion of grayscale values. The formula for the value is:

[0078] ;

[0079] The sum of the maximum and minimum grayscale values ​​of a pixel is taken as half of the value, and used as the parameter representing the baseline grayscale level of that pixel. The value of . Calculate the parameter characterizing the grayscale reference level. The formula for the value is:

[0080] .

[0081] For each pixel, the values ​​of the parameters representing the degree of grayscale dispersion and the parameters representing the grayscale baseline level are obtained in the same way.

[0082] To correct the actual interferometric image, firstly, the actual interferometric image obtained when using a light source of the target wavelength for interferometry is acquired, and the grayscale value of each pixel in the actual interferometric image is obtained. Then, based on the values ​​of the parameters representing the dispersion of grayscale values ​​for each pixel and the values ​​of the parameters representing the reference level of grayscale values, the grayscale values ​​of the corresponding pixels in the actual interferometric image are corrected to obtain the target interferometric image. Specifically, the correction process is as follows: The first difference between the grayscale value of the target pixel in the actual interferometric image and the value of the parameter representing the reference level of grayscale values ​​is obtained; where the target pixel is any pixel in the actual interferometric image.

[0083] Obtain the ratio between the first difference and the value of the parameter characterizing the degree of dispersion of gray values, and use the ratio as the corrected gray value corresponding to the target pixel;

[0084] The target interference image is obtained based on the corrected gray values ​​corresponding to each target pixel.

[0085] The formula for calculating the grayscale value of each pixel after correction is as follows:

[0086] ;

[0087] in, This represents the grayscale value of the target pixel in the actual interference image. This represents the grayscale value of the target pixel after intensity correction.

[0088] The light intensity was corrected for each pixel in the same way, and the normalized interference image, i.e. the target interference image, was obtained.

[0089] The method for intensity correction of interferometric images provided in this invention first acquires the grayscale values ​​of each pixel in a sample interferometric image dataset acquired by an interferometer under a light source of the target wavelength. Then, based on the grayscale values ​​of each pixel, the values ​​of a parameter characterizing the dispersion of grayscale values ​​and a parameter characterizing the reference level of grayscale values ​​are determined for each pixel. After acquiring the actual interferometric image obtained when using a light source of the target wavelength for interferometry, the grayscale values ​​of each pixel in the actual interferometric image are acquired. Finally, using the values ​​of the parameters characterizing the dispersion of grayscale values ​​and the reference level of grayscale values ​​for each pixel, the grayscale values ​​of the corresponding pixels in the actual interferometric image are corrected, thus achieving the correction of the grayscale values ​​of each pixel in the actual interferometric image and obtaining the target interferometric image. In other words, this method achieves the correction of the intensity of the interferometric image. Furthermore, compared to the method of using a built-in dedicated calibration device to block one path and then using the light spot corresponding to the unblocked optical path for light intensity correction, in this invention, the values ​​of the parameters representing the degree of grayscale dispersion and the parameters representing the grayscale reference level (i.e., correction coefficients) corresponding to each pixel are determined based on the grayscale values ​​of each pixel in the sample interferometric image dataset. This eliminates the need for a built-in calibration device to block one path, aligning with the actual interferometric measurement scenario. This avoids the problem of residual ripple error after correction and phase shift error correction when using these correction coefficients to correct the actual interferometric image, thus improving the measurement accuracy of the synthesized phase. Moreover, after obtaining the correction coefficients for each pixel, correction is performed pixel-by-pixel based on these coefficients, further improving the accuracy of the interferometric image correction and consequently enhancing the measurement accuracy of the synthesized phase.

[0090] In the above embodiments, processing is performed directly based on the acquired sample interferometric image dataset and the actual interferometric image. To further improve the accuracy of light intensity correction, after acquiring the sample interferometric image dataset, defect detection is performed on the pixels in the sample interferometric image dataset. The sample interferometric image dataset is denoted by I, and the new sample interferometric image dataset obtained after defect detection is denoted by I2. In one possible implementation, before determining the values ​​of the parameters characterizing the dispersion of grayscale values ​​and the parameters characterizing the reference level of grayscale values ​​for each pixel based on the grayscale values ​​of each pixel, the process further includes:

[0091] Obtain the average grayscale value of all pixels within the target area containing the preset pixel; where the preset pixel is any pixel in the sample interferometric image dataset;

[0092] Obtain the grayscale difference between the grayscale value of a preset pixel and the grayscale mean.

[0093] If the grayscale difference is detected to be greater than the threshold, a preset pixel is marked as an abnormal pixel, and the grayscale mean is used as the grayscale value of the preset pixel to obtain a new sample interference image dataset.

[0094] Correspondingly, based on the grayscale value of each pixel, the values ​​of the parameters representing the degree of grayscale value dispersion and the parameters representing the grayscale value baseline level for each pixel are determined as follows:

[0095] Based on the gray values ​​of each pixel in the new sample interferometric image dataset, the values ​​of the parameters representing the degree of dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined.

[0096] In this embodiment, by performing defect detection on the sample interference image dataset, the impact of abnormal pixels on the accuracy of light intensity correction is avoided, thereby improving the accuracy of light intensity correction.

[0097] Because the movement of the stitching mirror causes significant fluctuations in the grayscale values ​​of pixels in the interference fringe region, while the grayscale values ​​of pixels outside the stitching mirror fluctuate less due to the absence of reflected light from the mirror in the imaging process, to reduce data processing load, after obtaining a new sample interference image dataset, the stitching mirror region is further identified from the new dataset to obtain the effective region dataset I3, which is the image dataset corresponding to the stitching mirror region. In one possible implementation, based on the grayscale values ​​of each pixel in the new sample interference image dataset, the values ​​of the parameters representing the dispersion of grayscale values ​​and the parameters representing the baseline level of grayscale values ​​for each pixel are determined, including:

[0098] The stitching mirror region in the new sample interferometric image dataset is determined based on the gray values ​​of each pixel in the new sample interferometric image dataset.

[0099] Obtain the image dataset corresponding to the stitching mirror region in the new sample interference image dataset;

[0100] Based on the gray values ​​of each pixel in the image dataset corresponding to the stitching mirror region, the values ​​of the parameters representing the degree of dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined.

[0101] Specifically, determining the stitching mirror region in the new sample interferometric image dataset based on the gray values ​​of each pixel in the new sample interferometric image dataset includes:

[0102] Obtain the reference image from the new sample interferometric image dataset, as well as the remaining images other than the reference image; wherein the reference image is any image in the new sample interferometric image dataset;

[0103] Obtain the absolute value of the difference between the gray value of each pixel in the remaining image and the gray value of the corresponding pixel in the reference image;

[0104] Obtain the sum of the absolute values ​​of the differences corresponding to the same pixel in the remaining image, and determine the difference image based on the sum of the absolute values ​​of the differences corresponding to all pixels;

[0105] The difference image is binarized and morphological closing operation is performed to obtain the splicing mirror template image;

[0106] Using the stitching mirror template image, template matching is performed on all images in the new sample interferometric image dataset to obtain the stitching mirror region in the new sample interferometric image dataset.

[0107] In this embodiment, by identifying the stitching mirror region in the new sample interferometric image dataset, and then determining the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel based solely on the gray values ​​of each pixel in the image dataset corresponding to the stitching mirror region, compared to the method of determining the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel based on the gray values ​​of each pixel in the sample interferometric image dataset I or the new sample interferometric image dataset I2, the computational load is reduced, the resources occupied by light intensity correction are reduced, and the efficiency of light intensity correction is improved.

[0108] In one possible implementation, based on the gray values ​​of each pixel in the image dataset corresponding to the stitching mirror region, the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined, including:

[0109] In the image dataset corresponding to the splicing mirror region, curve fitting is performed on the gray value of the same pixel, and the maximum and minimum gray values ​​at the pixel position are determined based on the fitted curves.

[0110] A global bright map is created based on the maximum gray value corresponding to all pixel positions, and a global dark map is created based on the minimum gray value corresponding to all pixel positions.

[0111] Get the maximum and minimum grayscale values ​​at the same pixel location in the global bright and global dark images;

[0112] The value of the parameter representing the degree of dispersion of gray values ​​for each pixel is determined by obtaining half the difference between the maximum and minimum gray values ​​at the same pixel location.

[0113] The value of the parameter representing the grayscale baseline level corresponding to each pixel is determined by obtaining half of the sum of the maximum and minimum grayscale values ​​at the same pixel location.

[0114] The method of curve fitting is not limited, such as using a cosine function. For the values ​​of the parameters representing the dispersion of grayscale values ​​for each pixel, please refer to the description above. The calculation formula, and the values ​​of the parameters representing the grayscale reference level corresponding to each pixel, are described above. The calculation formula.

[0115] In the above embodiments, based on the gray values ​​of each pixel in the image dataset corresponding to the stitching mirror region, the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined. To improve the efficiency and accuracy of light intensity correction when correcting actual interferometric images, in one possible implementation, before obtaining the gray values ​​of each pixel in the actual interferometric image, the following steps are also included:

[0116] Abnormal pixel detection is performed on the actual interference image to obtain a new actual interference image;

[0117] The image data corresponding to the actual splicing mirror area is determined based on the gray values ​​of each pixel in the new actual interference image.

[0118] Obtaining the grayscale value of each pixel in the actual interference image includes:

[0119] Obtain the grayscale value of each pixel in the image data corresponding to the actual splicing mirror area.

[0120] The method for detecting abnormal pixels in the actual interference image and the process for determining the image data corresponding to the actual splicing mirror region are the same as the sample interference image processing process described above. Please refer to the description above; it will not be repeated here.

[0121] After obtaining the grayscale values ​​of each pixel in the image data corresponding to the actual stitching mirror area, the grayscale values ​​of the corresponding pixels in the actual interference image are corrected based on the values ​​of the parameters representing the dispersion of grayscale values ​​and the parameters representing the reference level of grayscale values ​​for each pixel, so as to obtain the target interference image including:

[0122] Obtain the first difference between the gray value of the target pixel in the actual interferometric image and the value of the parameter characterizing the gray value reference level; wherein, the target pixel is any pixel in the actual interferometric image;

[0123] Obtain the ratio between the first difference and the value of the parameter characterizing the degree of dispersion of gray values, and use the ratio as the corrected gray value corresponding to the target pixel;

[0124] The target interference image is obtained based on the corrected gray values ​​corresponding to each target pixel.

[0125] For the correction process of the gray values ​​of each pixel in the image data corresponding to the actual stitching mirror area, please refer to the description above. The calculation process.

[0126] The above method achieves intensity correction of an interferometric image at one wavelength. The multi-wavelength interferometer contains light sources with two or more measurement wavelengths. To achieve intensity correction of interferometric images at all wavelengths, in one possible implementation, after obtaining the target interferometric image, the following steps are also included:

[0127] The steps involve controlling the light source at the new target wavelength, returning to the process of acquiring the sample interference image dataset collected by the interferometer under the target wavelength light source, and obtaining the grayscale value of each pixel in the sample interference image dataset.

[0128] In the method provided in this embodiment, after correcting the interference image under one light source, the light source is changed, and the interference image under another light source is corrected, which further improves the measurement accuracy of the synthesized phase.

[0129] To enable those skilled in the art to better understand the entire process of the light intensity correction method for interferometric images provided by this invention, the description continues with reference to the accompanying drawings and specific embodiments. The multi-wavelength interferometer contains light sources with two or more measurement wavelengths. The light intensity consistency correction method for different wavelengths is consistent; the following description uses wavelength as an example. Taking an interference image of a light source as an example, the process of light intensity uniformity correction is introduced.

[0130] Figure 4 A flowchart illustrating the overall process of an intensity correction method for an interferometric image provided in an embodiment of the present invention is shown below. Figure 4 As shown, the method includes:

[0131] S10: Obtain the sample interferometric image dataset I for calibration;

[0132] S20: Perform image defect detection on sample interferometric image dataset I to obtain a new sample interferometric image dataset I2;

[0133] S30: Perform splicing mirror target detection on the new sample interferometric image dataset I2 to obtain the effective region dataset I3;

[0134] S40: Extract the global bright map and global dark map from the effective region dataset I3;

[0135] S50: Performs data processing on the global bright and dark images to obtain pixel-level correction coefficients. and ;

[0136] S60: Using a light source of the same wavelength for interferometry, an interference fringe image T is obtained. This image is then further processed through defect detection and effective region identification, and based on... and After intensity uniformity correction, the corrected interference fringes are obtained.

[0137] The steps are described in detail below.

[0138] For step S10, Figure 5 A flowchart illustrating a method for obtaining a sample interferometric image dataset for calibration, as provided in an embodiment of the present invention, is shown below. Figure 5 As shown, it includes:

[0139] S101: Align the optical axis of the interferometer with that of the spliced ​​telescope so that the interference fringes formed by the telescope are in the center field of view of the detector.

[0140] S102: Set the wavelength of the interferometer light source to 633nm, adjust the light source intensity and exposure time to achieve the best contrast in the interference fringe imaging and ensure that the pixel gray values ​​are not saturated.

[0141] S103: Control all hexapods connected to the splicing mirrors to make each splicing mirror move at a constant speed along the optical axis; during this period, the gray value of each pixel in the interference fringes should undergo at least 6 bright-dark cycles to ensure ergodicity;

[0142] S104: After the splicing mirror starts moving, control the interferometer to perform continuous imaging on the splicing mirror. The exposure time is consistent with the exposure time of subsequent interferometric measurements to obtain the sample interferometric image dataset I.

[0143] When using an interferometer to perform interferometric imaging on a splicing mirror, in order to reduce the interference of vibrating airflow, the single exposure time of the interferometer camera should not be too long, and should be controlled within 1.5ms to 3ms.

[0144] To ensure that the grayscale value of each pixel in the interference fringes undergoes at least 6 bright-dark cycles during the movement, preferably, the moving speed of the splicing mirror is set to about 1 / 6 wavelength / second, and the moving duration is ≥18 seconds.

[0145] During the movement of the splicing mirror, the number of images N captured by the interferometer should be no less than 500. To ensure that the camera readout rate meets the requirements, a polarization camera with a high readout frame rate should be selected, such as a polarization camera with a fastest readout frame rate of 75fps.

[0146] For step S20, defect detection in dataset I. If the absolute value of the difference between the gray value of a pixel and the average gray value of pixels in the 5×5 neighborhood is greater than the threshold T, it is marked as an abnormal pixel (i.e., a bad pixel), and the average gray value is used to fill the gap, resulting in a new sample interferometric image dataset I2. In practice, the threshold T can be determined based on the detector quantization bit depth and the detector noise level at room temperature.

[0147] Because the movement of the splicing mirror causes significant fluctuations in the grayscale values ​​of pixels in the interference fringe region, while the grayscale values ​​of pixels outside the splicing mirror fluctuate less due to the absence of reflected light from the mirror in the imaging process. Therefore, the splicing mirror region is identified by analyzing the pixel differences at corresponding positions in the new sample interference image dataset I2, resulting in the effective region dataset I3. For step S30, Figure 6 A flowchart of a method for performing stitching mirror target detection on a new sample interferometric image dataset I2 to obtain an effective region dataset I3 is provided in an embodiment of the present invention, as follows: Figure 6 As shown, the method includes:

[0148] S301: Take the first image in the new sample interferometric image dataset I2 as the reference image I_r, and calculate the absolute value of the difference between the other images and the reference image I_r, and then superimpose them to obtain the difference image I_d;

[0149] S302: Binarize the difference image I_d and perform morphological closing operation to eliminate small holes in the region and obtain the splicing mirror template;

[0150] S303: Perform template matching on all images in the new sample interference image dataset I2 to obtain the effective region dataset I3.

[0151] For step S40, Figure 7 A flowchart illustrating a method for extracting global bright and global dark maps from a dataset I3 of an effective region, as provided in this embodiment of the invention, is shown below. Figure 7 As shown, the method includes:

[0152] S401: Fit a cosine function to the pixels at position (x,y) in the image sequence of the effective region dataset I3 based on their gray values;

[0153] S402: Calculate the maximum and minimum gray values ​​at each pixel position (x,y) based on the fitted curve. Construct a global bright map based on the maximum gray values ​​of all pixels and a global dark map based on the minimum gray values ​​of all pixels.

[0154] The expression for the cosine function fitting is:

[0155] ;

[0156] in, Indicates amplitude, Indicates spatial frequency, Indicates the measurement sequence number. Indicates the initial phase. This represents the average grayscale value without modulation.

[0157] For step S50, for the global bright map and the global dark map, according to The calculation formula and The calculation formula is used to process the data and obtain pixel-level correction coefficients. and It is worth noting that smoothing is prohibited for both the bright and dark areas during global bright and dark area calculations to avoid losing the true grayscale difference information between pixels.

[0158] For step S60, an interference measurement is performed using a light source of the same wavelength to obtain an interference fringe image T. Each interference fringe image is processed according to the methods provided in steps S20 and S30 to obtain an effective region interference fringe image T1. Then, according to... and Perform intensity consistency correction on the interference fringe pattern T1.

[0159] The above is based on wavelength. Taking an interference image of a light source as an example, we will complete its intensity correction process. After correcting one interference image, we will continue with... The light intensity is corrected by sampling other amplitude interference images of the light source using the method described above, that is, the light intensity is corrected for wavelengths of [wavelength value missing]. The light intensity of all interference images of the light source was corrected, achieving the correction of light intensity for wavelengths of [wavelength value missing]. The intensity of light in all interference images of the light source was normalized to ensure that the wavelength was [value missing]. The light intensity uniformity of all interference images of the light source.

[0160] The correction method for interference images at other wavelengths is the same as that provided above for wavelengths. The correction method for interference images under the same light source is consistent. After processing, the interference fringes of each wavelength have a consistent fluctuation range, completing the intensity consistency correction of interference fringes of all wavelengths.

[0161] The light intensity correction method provided by this invention has a simple and efficient data processing process during synchronous phase-shift interferometry measurement of splicing mirrors. It does not require a dedicated calibration device and can achieve light intensity consistency correction based solely on the interferometric image. It can also ensure the consistency between calibration and measurement scenarios. It can effectively identify and remove the interference of pixel defects on pixel-level correction. It uses a pixel-by-pixel cosine function fitting method to obtain the maximum and minimum values, thereby improving the accuracy of light intensity consistency correction.

[0162] In the above embodiments, the method for correcting the light intensity of interferometric images has been described in detail. This invention also provides embodiments of an interferometric image light intensity correction device and an electronic device. It should be noted that this invention describes the device embodiments from two perspectives: one based on functional modules, and the other based on hardware.

[0163] This invention provides a light intensity correction device for interferometric images. This embodiment is based on functional modules and includes:

[0164] The first acquisition module is used to acquire the sample interference image dataset collected by the interferometer under the light source of the target wavelength, and to acquire the gray value of each pixel in the sample interference image dataset;

[0165] The determination module is used to determine the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel, based on the gray values ​​of each pixel.

[0166] The second acquisition module is used to acquire the actual interference image obtained when using a light source with the target wavelength for interferometry, and to acquire the gray value of each pixel in the actual interference image.

[0167] The correction module is used to correct the gray values ​​of corresponding pixels in the actual interference image based on the values ​​of the parameters representing the degree of gray value dispersion and the parameters representing the gray value reference level, so as to obtain the target interference image.

[0168] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0169] Figure 8 This is a structural diagram of an electronic device provided in an embodiment of the present invention. This embodiment is based on a hardware perspective, such as... Figure 8 As shown, the electronic device includes:

[0170] Memory 20 is used to store computer programs;

[0171] The processor 21 is configured to execute a computer program to implement the steps of the method for intensity correction of an interferometric image as described in the above embodiments.

[0172] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0173] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the intensity correction method for interferometric images disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, the data involved in the intensity correction method for interferometric images mentioned above.

[0174] In some embodiments, the electronic device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0175] Those skilled in the art will understand that Figure 8 The structures shown do not constitute a limitation on electronic devices and may include more or fewer components than those shown.

[0176] The electronic device provided in this embodiment of the invention includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method: a light intensity correction method for an interference image, with the same effect as above.

[0177] Finally, the present invention also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps described in the above method embodiments.

[0178] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0179] The computer-readable storage medium provided by this invention includes the light intensity correction method for interference images mentioned above, with the same effect.

[0180] The above provides a detailed description of the light intensity correction method and electronic device for interferometric images provided by the present invention. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principle of the invention, and these improvements and modifications also fall within the protection scope of the present invention.

[0181] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for intensity correction of an interferometric image, characterized in that, include: Acquire a dataset of sample interference images collected by the interferometer under a light source of the target wavelength, and obtain the gray value of each pixel in the sample interference image dataset; Based on the gray values ​​of each pixel, determine the values ​​of the parameters that characterize the degree of dispersion of gray values ​​and the parameters that characterize the baseline level of gray values ​​for each pixel; Obtain the actual interference image obtained when using a light source with the target wavelength for interferometry, and obtain the gray value of each pixel in the actual interference image; Based on the values ​​of the parameters representing the degree of grayscale dispersion corresponding to each pixel and the values ​​of the parameters representing the baseline level of grayscale values, the grayscale values ​​of the corresponding pixels in the actual interference image are corrected to obtain the target interference image.

2. The method for intensity correction of interferometric images according to claim 1, characterized in that, The dataset of sample interferometric images acquired by the interferometer under a light source of the target wavelength includes: With the intensity of the light source at the target wavelength controlled to the target intensity and the exposure time controlled to the target exposure time, the movement of the moving device connected to the splicing mirror is controlled to drive the splicing mirror to move along the optical axis. During the movement of the splicing mirror, the interferometer is controlled to perform multiple imaging operations on the splicing mirror to obtain the sample interference image dataset.

3. The method for intensity correction of interferometric images according to claim 1, characterized in that, Before determining the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel, based on the gray values ​​of each pixel, the process also includes: Obtain the average grayscale value of all pixels within the target area where the preset pixel is located; wherein, the preset pixel is any pixel in the sample interference image dataset; Obtain the absolute value of the grayscale difference between the grayscale value of the preset pixel and the grayscale mean value; If the absolute value of the gray difference is detected to be greater than the threshold, the preset pixel is marked as an abnormal pixel, and the gray mean is used as the gray value of the preset pixel to obtain a new sample interference image dataset. The determination of the values ​​of the parameters representing the degree of dispersion of gray values ​​for each pixel and the parameters representing the reference level of gray values ​​based on the gray values ​​of each pixel includes: Based on the gray values ​​of each pixel in the new sample interferometric image dataset, the values ​​of the parameters representing the degree of dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined.

4. The method for intensity correction of interferometric images according to claim 3, characterized in that, The determination of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel based on the gray values ​​of each pixel in the new sample interferometric image dataset includes: The stitching mirror region in the new sample interferometric image dataset is determined based on the gray values ​​of each pixel in the new sample interferometric image dataset. Obtain the image dataset corresponding to the stitching mirror region in the new sample interference image dataset; Based on the gray values ​​of each pixel in the image dataset corresponding to the stitching mirror region, the values ​​of the parameters representing the degree of dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined.

5. The method for intensity correction of interferometric images according to claim 4, characterized in that, The process of determining the stitching mirror region in the new sample interferometric image dataset based on the gray values ​​of each pixel in the new sample interferometric image dataset includes: Obtain a reference image from the new sample interferometric image dataset, as well as the remaining images other than the reference image; wherein the reference image is any image in the new sample interferometric image dataset; Obtain the absolute value of the difference between the gray value of each pixel in the remaining image and the gray value of the corresponding pixel in the reference image; Obtain the sum of the absolute values ​​of the differences corresponding to the same pixel in the remaining image, and determine the difference image based on the sum of the absolute values ​​of the differences corresponding to all pixels; The difference image is binarized and morphological closing is performed to obtain the splicing mirror template image; Using the stitching mirror template image, template matching is performed on all images in the new sample interferometric image dataset to obtain the stitching mirror region in the new sample interferometric image dataset.

6. The method for intensity correction of interferometric images according to claim 4, characterized in that, Based on the gray values ​​of each pixel in the image dataset corresponding to the stitching mirror region, the values ​​of the parameters representing the dispersion of gray values ​​and the parameters representing the baseline level of gray values ​​for each pixel are determined, including: In the image dataset corresponding to the splicing mirror region, curve fitting is performed on the gray value of the same pixel, and the maximum and minimum gray values ​​at the pixel position are determined based on the fitted curves. A global bright map is created based on the maximum gray value corresponding to all pixel positions, and a global dark map is created based on the minimum gray value corresponding to all pixel positions. Obtain the maximum and minimum grayscale values ​​at the same pixel location in the global bright image and the global dark image; The value of the parameter representing the degree of dispersion of gray values ​​for each pixel is determined by obtaining half the difference between the maximum and minimum gray values ​​at the same pixel location. The value of the parameter representing the grayscale baseline level corresponding to each pixel is determined by obtaining half of the sum of the maximum and minimum grayscale values ​​at the same pixel location.

7. The method for intensity correction of an interferometric image according to any one of claims 1 to 6, characterized in that, Before obtaining the grayscale value of each pixel in the actual interference image, the process also includes: Abnormal pixel detection is performed on the actual interference image to obtain a new actual interference image; The image data corresponding to the actual splicing mirror area is determined based on the gray values ​​of each pixel in the new actual interference image. Obtaining the grayscale value of each pixel in the actual interference image includes: Obtain the grayscale value of each pixel in the image data corresponding to the actual splicing mirror area.

8. The method for intensity correction of an interferometric image according to any one of claims 1 to 6, characterized in that, Based on the values ​​of the parameters representing the dispersion of gray values ​​corresponding to each pixel and the values ​​of the parameters representing the reference level of gray values, the gray values ​​of the corresponding pixels in the actual interferometric image are corrected to obtain the target interferometric image, including: Obtain the first difference between the gray value of the target pixel in the actual interferometric image and the value of a parameter characterizing the gray value reference level; wherein, the target pixel is any pixel in the actual interferometric image; Obtain the ratio between the first difference and the value of the parameter characterizing the degree of dispersion of gray values, and use the ratio as the corrected gray value corresponding to the target pixel; The target interference image is obtained based on the corrected gray values ​​corresponding to each target pixel.

9. The method for intensity correction of interferometric images according to claim 8, characterized in that, After obtaining the target interferometric image, the following is also included: The steps include controlling the light source to operate at the new target wavelength, returning to the step of acquiring the sample interference image dataset collected by the interferometer under the target wavelength light source, and acquiring the grayscale value of each pixel in the sample interference image dataset.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the light intensity correction method for an interferometric image as described in any one of claims 1 to 9 when executing the computer program.

Images