Point target wide-area high-precision positioning method, device and equipment combining fine calibration and coarse calibration of imaging model

Abstract

The invention provides a point target wide-area high-precision positioning method, device and equipment combining fine and coarse calibration of an imaging model, relates to the technical field of optical imaging type precision measurement, and aims to realize point target wide-area high-precision positioning. The method comprises the following steps: acquiring point target images at different sub-pixel positions, and calibrating a PSF of a first pixel on an image detector to obtain a reference PSF model; mapping the reference PSF model to a second pixel, and fitting the reference PSF model with imaging data of the second pixel to obtain an extended PSF model; wherein the extended PSF model represents pixel response values corresponding to different positions from an image spot center to a pixel center; and positioning a wide-area point target image acquired by the image detector through the extended PSF model and a maximum likelihood method to obtain a positioning result.

Description

Technical Field

[0001] The present application relates to the field of optical imaging precision measurement technology, and in particular to a method, device and equipment for wide-area high-precision positioning of point targets that combines fine and coarse calibration of an imaging model. Background Art

[0002] In the field of optical imaging measurement, the imaging of any object can be viewed as a superposition of point target images, that is, the superposition of the optical system's PSF (Point Spread Function). Therefore, point targets are the most basic and typical optical targets. Calculating the imaging position of a point target on an image detector is called point target positioning technology, which directly determines the ultimate measurement accuracy of optical instruments in many applications. In scenarios such as space navigation and astronomical observation, for example, star sensors calculate spacecraft attitude by matching star imaging with star catalogs. The accuracy of their attitude measurement is positively correlated with the accuracy of star positioning.

[0003] The core of high-precision positioning of point targets is to establish an accurate imaging PSF model. For example, the Hubble Telescope uses multi-frame star image inversion experimental PSFs and combines them with least squares fitting to reduce model errors. However, PSF calibration faces two major technical bottlenecks: First, traditional laboratory calibration requires the use of precision motion devices to generate sub-pixel displacements to improve the spatial sampling rate. However, due to the non-uniformity of the pixel response of the image detector, the PSF model calibrated by a single pixel cannot be adapted to other pixels, resulting in poor model reusability; second, to achieve wide-area high-precision positioning, the entire imaging area needs to be calibrated pixel by pixel, generating massive amounts of model data and causing complex calibration processes. Therefore, due to the limitations of pixel response non-uniformity, the existing methods of calibrating PSF models have high calibration costs and small application areas, making it difficult to achieve wide-area high-precision positioning of point targets. Summary of the Invention

[0004] In view of the above problems, the embodiments of the present application provide a method, device and equipment for wide-area high-precision positioning of point targets that combines fine and coarse calibration of an imaging model, so as to overcome the above problems or at least partially solve the above problems.

[0005] In a first aspect of the embodiments of the present application, a method for wide-area high-precision positioning of point targets combining fine and coarse calibration of an imaging model is disclosed, the method comprising: Acquire point target images at different sub-pixel positions, calibrate the point spread function (PSF) of the first pixel on the image detector, and obtain a reference PSF model; wherein the first pixel is any pixel on the image detector; Mapping the reference PSF model to a second pixel and fitting it with imaging data of the second pixel to obtain an extended PSF model; wherein the second pixel is any pixel other than the first pixel on the image detector, the imaging data is response values ​​of the second pixel acquired when the center of the imaging spot is located at multiple different positions within an image region near the center of the second pixel, and the extended PSF model represents pixel response values ​​corresponding to different positions of the image spot center from the pixel center; The wide-area point target image collected by the image detector is positioned by using the extended PSF model and the maximum likelihood method to obtain a positioning result.

[0006] Optionally, point target images at different sub-pixel positions are collected, and the point spread function (PSF) of the first pixel on the image detector is calibrated to obtain a reference PSF model, including: collecting multiple frames of point target images at different sub-pixel positions within a target window centered on the first pixel; Calculating a pixel response value corresponding to each sub-pixel position based on the multiple frames of point target images collected at each sub-pixel position to obtain an imaging PSF sampling matrix for the first pixel, wherein each element in the imaging PSF sampling matrix represents a pixel response value corresponding to a different position of the image spot center from the center of the first pixel; An interpolation process is performed on the imaging PSF sampling matrix to obtain a reference PSF model.

[0007] Optionally, acquiring multiple frames of point target images at different sub-pixel positions within a target window centered on the first pixel includes: Using a motion actuator to move the image detector, thereby forming a relative micro-motion between the point target and the image detector, and controlling the image spot of the point target to perform a sub-pixel micro-displacement within a target window centered on the first pixel; Multi-frame point target image sampling is performed at each sub-pixel position to obtain multi-frame point target images collected at different sub-pixel positions within the first pixel.

[0008] Optionally, mapping the reference PSF model to a second pixel and fitting it with imaging data of the second pixel to obtain an extended PSF model includes: Moving the point target image spot within a target window centered on the second pixel, and collecting imaging data at multiple target positions to obtain imaging data of the second pixel; Mapping the reference PSF model to a second pixel, and predicting a pixel response of the second pixel based on the reference PSF model to obtain a predicted pixel response value of the second pixel; The predicted pixel response value of the second pixel and the imaging data of the second pixel are fitted using a least squares method to obtain an extended PSF model.

[0009] Optionally, the predicted pixel response value of the second pixel includes predicted pixel response values ​​corresponding to a plurality of target positions; mapping the benchmark PSF model to the second pixel, and predicting the pixel response value of the second pixel according to the benchmark PSF model to obtain the predicted pixel response value of the second pixel includes: Mapping the reference PSF model to the second pixel, and determining reference positions corresponding to the plurality of target positions from the reference PSF model; The pixel response value corresponding to the reference position in the reference PSF model is used as the predicted pixel response value corresponding to the target position in the second pixel.

[0010] Optionally, positioning the wide-area point target image collected by the image detector using the extended PSF model and the maximum likelihood method to obtain a positioning result includes: Extracting an imaging area from the wide-area point target image and removing an image background value of the imaging area to obtain a pixel response matrix of the imaging area; Establishing a joint probability distribution of a pixel response matrix of the imaging area according to the extended PSF model; Based on the maximum likelihood method and according to the joint probability distribution, the image spot position with the maximum probability of occurrence of the pixel response matrix in the imaging area is calculated as the positioning result.

[0011] Optionally, establishing a joint probability distribution of the imaging area pixel response matrix according to the extended PSF model includes: Determining the number of photons detected during the pixel response process to obey a Poisson distribution, and determining a mean of the Poisson distribution according to the extended PSF model; determining a pixel response probability density function of a third pixel according to the mean of the Poisson distribution, where the third pixel is any pixel in the imaging area; The pixel response probability density functions of all pixels in the imaging area are multiplied to obtain the joint probability distribution.

[0012] Optionally, based on the maximum likelihood method, according to the joint probability distribution, calculating the image spot position that maximizes the probability of the pixel response matrix of the imaging area appearing as the positioning result includes: Constructing a cost function according to the joint probability distribution; The cost function is minimized using an iterative Newton-Raphson method, and a positioning result is obtained when an iteration end condition is met; The item in the cost function related to the image spot position Expressed as: , Where i and j represent the row and column of the pixel respectively, K represents the pixel gain, Represents pixels The relative response intercept relative to the first pixel, Indicates the distance between the image spot position and the second pixel center The pixel response value, Indicates the actual pixel response value of the second pixel collected.

[0013] A second aspect of the embodiments of the present application discloses a device for wide-area high-precision positioning of point targets combining fine and coarse calibration of an imaging model, the device comprising: a calibration module, configured to acquire point target images at different sub-pixel positions and calibrate the point spread function (PSF) of a first pixel on an image detector to obtain a reference PSF model; wherein the first pixel is any pixel on the image detector; a fitting module, configured to map the reference PSF model to a second pixel and fit the second pixel's imaging data to obtain an extended PSF model; wherein the second pixel is any pixel on the image detector other than the first pixel, the imaging data is response values ​​of the second pixel acquired when the imaging spot center is located at a plurality of different positions within an image region near the center of the second pixel, and the extended PSF model represents pixel response values ​​corresponding to different positions of the spot center from the pixel center; The positioning module is used to locate the wide-area point target image collected by the image detector by using the extended PSF model and the maximum likelihood method to obtain a positioning result.

[0014] The third aspect of the embodiments of the present application discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the method for wide-area high-precision positioning of point targets combining fine and coarse calibration of the imaging model described in the first aspect of the embodiments of the present application are implemented.

[0015] The fourth aspect of the embodiments of the present application discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for wide-area high-precision positioning of point targets combining fine and coarse calibration of the imaging model described in the first aspect of the embodiments of the present application are implemented.

[0016] The fifth aspect of the embodiments of the present application discloses a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for wide-area high-precision positioning of point targets combining fine and coarse calibration of the imaging model as described in the first aspect of the embodiments of the present application.

[0017] The embodiments of the present application include the following advantages: In an embodiment of the present application, point target images at different sub-pixel positions are collected, and the point spread function (PSF) of the first pixel on the image detector is calibrated to obtain a finely calibrated reference PSF model. Based on the finely calibrated reference PSF model, the reference PSF model is mapped to the second pixel and fitted with the imaging data of the second pixel to establish an extended PSF model for other pixels. The extended PSF model characterizes the pixel response values ​​corresponding to different positions of the image spot center from the pixel center. Therefore, this method uses the coarsely calibrated PSF extended model as an approximation, while ensuring the accuracy of the PSF model, reducing the calibration cost and expanding the applicable area of ​​the precise PSF model, making it suitable for practical engineering applications. In addition, by using the extended PSF model and the maximum likelihood method, the wide-area point target image collected by the image detector is positioned, making full use of the distribution characteristics of random noise, achieving point target positioning measurement close to the theoretical accuracy limit, and improving the accuracy of point target positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flowchart of the steps of a method for wide-area high-precision positioning of point targets that combines fine and coarse calibration of an imaging model provided in an embodiment of the present application; Figure 2 This is a schematic diagram of a rough calibration result of pixel unevenness provided in an embodiment of the present application; Figure 3 This is a comparison chart of the PSF rough calibration effect provided in an embodiment of the present application; Figure 4 This is a flowchart of the steps of another method for wide-area high-precision positioning of point targets that combines fine and coarse calibration of an imaging model provided in an embodiment of the present application; Figure 5 This is a schematic diagram of a point target wide-area high-precision positioning result provided by an embodiment of the present application; Figure 6 This is a schematic diagram of the structure of a point target wide-area high-precision positioning device that combines fine and coarse calibration of an imaging model, provided in an embodiment of the present application; Figure 7 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] To make the above-mentioned purposes, features, and advantages of this application more clearly understood, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of this application.

[0021] Point target positioning methods in related technologies can be divided into two categories. The first category is the centroid method, which calculates the first-order grayscale moment of the pixel area in the star point imaging area that meets a certain energy response threshold. The method is simple and fast to calculate, but because the centroid of the pixel is used instead of the centroid of the light intensity distribution within the pixel, this method has an S-shaped systematic error and low accuracy. The second method is the fitting method, which fits different PSF functions to the measured pixel data to obtain the center position of the image spot. The most commonly used fitting method is the Gaussian fitting method. This method assumes that the pixel data of star imaging can be approximated as a Gaussian function or an integral function of a Gaussian function. It has a large amount of calculation and has good accuracy in the ideal case where the actual PSF is not much different from the Gaussian function, but for real non-ideal PSFs, the accuracy is often greatly reduced.

[0022] Therefore, the core of high-precision positioning of point targets is to establish an accurate imaging PSF model. However, due to the limitations of pixel response non-uniformity, existing methods for calibrating PSF models have high calibration costs and small application areas, making it difficult to achieve wide-area high-precision positioning of point targets. In order to overcome the lack of wide-area high-precision positioning methods for point targets in related technologies, the embodiments of the present application provide a wide-area high-precision positioning method for point targets that combines fine and coarse calibration of an imaging model, so as to reduce calibration costs while ensuring PSF accuracy, expand the applicable area of ​​the accurate PSF model, and thus be suitable for actual engineering applications.

[0023] Reference Figure 1 As shown, Figure 1 This is a flowchart of the steps of a method for wide-area high-precision positioning of point targets that combines fine and coarse calibration of an imaging model provided in an embodiment of the present application. Figure 1 As shown, the method for wide-area high-precision positioning of point targets by combining fine and coarse calibration of the imaging model may include steps S110 to S130: Step S110: collecting point target images at different sub-pixel positions, calibrating the point spread function (PSF) of the first pixel on the image detector, and obtaining a reference PSF model; wherein the first pixel is any pixel on the image detector.

[0024] The point target image refers to an image captured for a point target (optical point target). The point target image contains an image spot corresponding to the point target, and the center position of the image spot is the position of the point target. Specifically, any pixel on the image detector is determined as the first pixel, and the first pixel is divided into multiple ( ) sub-pixel position grid, which can control the movement of the image spot to different sub-pixel positions within the first pixel, and then collect point target images located at different sub-pixel positions.

[0025] Based on point target images at different sub-pixel positions, the pixel response corresponding to each sub-pixel position is calculated. A reference PSF sampling matrix is ​​then obtained based on the pixel response corresponding to each sub-pixel position within the first pixel. This sampling matrix is ​​then interpolated to obtain a reference PSF model. This reference PSF model is a pixel response value (PSF value) with a continuous distance. This means that any position from the image spot center to the center of the first pixel has a corresponding pixel response value (the reference PSF model can accurately represent the correspondence between different pixel phases and pixel responses). Therefore, the reference PSF model is a precisely calibrated reference PSF model.

[0026] Step S120: Mapping the baseline PSF model to a second pixel and fitting it with the imaging data of the second pixel to obtain an extended PSF model; wherein the second pixel is any pixel other than the first pixel on the image detector, the imaging data is the response value of the second pixel collected when the center of the imaging spot is located at multiple different positions in the image area near the center of the second pixel, and the extended PSF model represents the pixel response values ​​corresponding to different positions of the image spot center from the pixel center.

[0027] The extended PSF model is a PSF model that is coarsely calibrated for pixel inhomogeneity. Because the extended PSF model is obtained by fitting the finely calibrated baseline PSF model and the imaging data of the second pixel, it can be approximated to a precise PSF model. The pixel responses of any pixels (second pixels) on the image detector other than the first pixel are calibrated using the method of step S120.

[0028] Specifically, the target window centered at the second pixel (eg, the target window centered at the second pixel) may be selected. Imaging data is collected at multiple target positions within a range of , that is, a point target image is collected at each target position, and the pixel response value of the target position is calculated based on the point target image; then, the baseline PSF model is mapped to the second pixel to obtain a predicted pixel response, and the predicted pixel response and the imaging data of the second pixel are fitted based on a linear model of the pixel response to obtain an extended PSF model.

[0029] Step S130: positioning the wide-area point target image collected by the image detector using the extended PSF model and the maximum likelihood method to obtain a positioning result.

[0030] The wide-area point target image captured by the image detector contains the image spot corresponding to the point target. The center of the image spot is the location of the point target. For wide-area point target images, the imaging area (i.e., the area where the image spot is located) can be extracted. This area is then located using the extended PSF model and maximum likelihood method to calculate the image spot position and obtain the positioning result.

[0031] Through the above implementation process, point target images at different sub-pixel positions are acquired, and the point spread function (PSF) of the first pixel on the image detector is calibrated to obtain a finely calibrated reference PSF model. Based on this finely calibrated reference PSF model, the reference PSF model is mapped to the second pixel and fitted with the imaging data of the second pixel to establish an extended PSF model for other pixels. This extended PSF model represents the pixel response values ​​corresponding to different positions of the image spot center from the pixel center. Therefore, this method uses the coarsely calibrated extended PSF model as an approximation, while maintaining the accuracy of the PSF model while reducing calibration costs and expanding the applicable area of ​​the fine PSF model, making it suitable for practical engineering applications. Furthermore, through the extended PSF model and the maximum likelihood method, wide-area point target images acquired by the image detector are positioned, fully utilizing the distribution characteristics of random noise to achieve point target positioning measurement close to the theoretical accuracy limit, thereby improving the accuracy of point target positioning.

[0032] In combination with the above embodiments, in one embodiment, the present application also provides a method for wide-area high-precision positioning of point targets by combining fine and coarse calibration of an imaging model. In this method, the above step S110 of "collecting point target images at different sub-pixel positions, calibrating the point spread function (PSF) of the first pixel on the image detector, and obtaining a reference PSF model" specifically includes sub-steps S110-1 to S110-3: Step S110 - 1 : Capturing multiple frames of point target images at different sub-pixel positions within a target window centered on the first pixel.

[0033] In the embodiment of the present application, the target window centered on the first pixel Divide into multiple ( ) sub-pixel position grid, controlling the image spot to move at different sub-pixel positions, so that the image spot position traverses ( ) sub-pixel positions, and perform multi-frame (for example, 30 frames) point target images on each sub-pixel position. The sub-pixel position grid divided by the window centered on the first pixel is determined according to the energy concentration of the PSF. For example, the first pixel can be divided into A sub-pixel location network.

[0034] In some embodiments, multiple frames of point target images are collected at different sub-pixel positions within a target window centered on the first pixel, including: using a motion actuator to move the image detector to form a relative micro-motion between the point target and the image detector, and controlling the image spot of the point target to perform sub-pixel micro-displacement within the target window centered on the first pixel; and sampling multiple frames of point target images at each sub-pixel position to obtain multiple frames of point target images collected at different sub-pixel positions within the first pixel.

[0035] The target window is an area consisting of multiple pixels centered at the first pixel, for example, the target window Can be pixels or The motion actuator is a high-precision turntable or a nano-pressure stage. Specifically, the high-precision turntable can be used to rotate the optical measurement sensor, or the nano-pressure stage can be used to move the image detector, so as to form a relative micro-motion between the point target and the image detector, so that the image spot is moved in the first pixel ( ) is the center The sub-pixel step size in the target window is The micro displacement makes the image spot position traverse Finally, multiple frames of point target images are collected at different sub-pixel positions within the target window centered on the first pixel.

[0036] Step S110-2: Based on the multi-frame point target images collected at each sub-pixel position, calculate the pixel response value corresponding to each sub-pixel position to obtain the imaging PSF sampling matrix of the first pixel, where each element in the imaging PSF sampling matrix represents the pixel response value corresponding to different positions of the image spot center from the center of the first pixel.

[0037] Among them, the imaging PSF sampling matrix of the first pixel is a point target located at the interval The imaging PSF sampling matrix of the sub-pixel position grid (sub-pixel step size) can be calculated. The pixel response value of each frame of the point target image can be calculated separately, and the average of the pixel response values ​​is calculated based on the pixel response value of each frame of the point target image as the pixel response value corresponding to the sub-pixel position; based on the pixel response value corresponding to each sub-pixel position, the imaging PSF sampling matrix of the first pixel is obtained.

[0038] For example, the imaging PSF sampling matrix of the first pixel can be expressed as:

[0039] in, , , Indicates the initial position of the image spot distance from the center of the first pixel; Indicates the distance between the image spot center and the pixel center When the pixel response value is large, the grayscale signal-to-noise ratio is high, and the grayscale signal-to-noise ratio is high. is a sample value of the imaging PSF.

[0040] Step S110 - 3 : performing interpolation processing on the imaging PSF sampling matrix to obtain a reference PSF model.

[0041] Among them, the imaging PSF sampling matrix of the first pixel is a point target located at the interval The imaging PSF sampling matrix of the sub-pixel position grid is used. In order to obtain a benchmark PSF model with a continuous distance pixel response value, the cubic spline interpolation technology with non-node boundary conditions is used to interpolate the imaging PSF sampling matrix of the first pixel to obtain an accurately calibrated benchmark PSF model. .

[0042] Through the above implementation process, the benchmark PSF model corresponding to the first pixel is calibrated based on multi-frame point target images at different sub-pixel positions. Sampling multi-frame point target images reduces the influence of random noise and removes the image background value, thereby improving the accuracy of the benchmark PSF model and achieving accurate calibration of the benchmark PSF model.

[0043] In combination with the above embodiments, in one embodiment, the embodiments of the present application further provide a method for wide-area high-precision positioning of point targets combining fine and coarse calibration of an imaging model. In this method, the above step S120 of "mapping the reference PSF model to a second pixel and fitting it with the imaging data of the second pixel to obtain an extended PSF model" specifically includes sub-steps S120-1 to S120-3: Step S120 - 1 : moving the point target image spot within a target window centered on the second pixel, and collecting imaging data at multiple target positions to obtain imaging data of the second pixel.

[0044] The target window centered on the second pixel may be a window centered on the second pixel. The target image spot is moved in the target window with the second pixel as the center, so that the second pixel The pixel response of the target image changes significantly, and imaging data is collected at multiple (for example, 2 to 3) target locations. Specifically, multiple frames (for example, 30 frames) of point target images are collected at each target location, and the pixel response value of each frame of the point target image is calculated respectively, and then the average of the pixel response values ​​of the multiple frames of the point target image is used as the pixel response value collected at the target location. For example, the imaging data of the second pixel It can be expressed as:

[0045] in, Represents the pixel response value collected at the first target position, Represents the pixel response value collected at the ath target position.

[0046] It is understandable that the multiple target positions for imaging data acquisition can be determined based on energy values, that is, the energy peak position, the position with higher energy, and the position with very low energy can be respectively used as a target position. For example, if imaging data is acquired at three target positions, the point target can be located at the center of the second pixel, the edge of the second pixel, and any pixel far from the second pixel. is the energy peak sampling point, is a sampling point with higher energy, The energy sampling point is very low.

[0047] Step S120 - 2 : Mapping the reference PSF model to a second pixel, and predicting a pixel response of the second pixel according to the reference PSF model to obtain a predicted pixel response value of the second pixel.

[0048] Among them, the predicted pixel response value of the second pixel includes predicted pixel response values ​​corresponding to multiple target positions; specifically, mapping the benchmark PSF model to the second pixel, and predicting the pixel response value of the second pixel according to the benchmark PSF model to obtain the predicted pixel response value of the second pixel, including: mapping the benchmark PSF model to the second pixel, and determining the benchmark positions corresponding to the multiple target positions from the benchmark PSF model respectively; using the pixel response value corresponding to the benchmark position in the benchmark PSF model as the predicted pixel response value corresponding to the target position in the second pixel.

[0049] For example, the second pixel The predicted pixel response value It can be expressed as:

[0050] in, Represents the predicted pixel response value corresponding to the first target position, Indicates in The predicted pixel response value corresponding to each target position.

[0051] Step S120 - 3 : fitting the predicted pixel response value of the second pixel and the imaging data of the second pixel using the least squares method to obtain an extended PSF model.

[0052] Among them, the fitting equation (i.e. the linear model of pixel response) can be expressed as:

[0053] According to the predicted pixel response value of the second pixel and the imaging data of the second pixel, the solution is obtained. and ;in, Represents the second pixel Relative to the first pixel ( )’s relative response slope, Represents the second pixel Relative to the first pixel ( ), the fitting results are as follows Figure 2 As shown, the extended PSF model for coarse calibration of pixel inhomogeneity can be obtained by fitting the predicted pixel response values ​​of multiple target positions with the collected pixel response values.

[0054] For example, the extended PSF model can be expressed as:

[0055] In this way, based on the benchmark PSF model of fine calibration of the first pixel, the imaging data sampled at multiple target sampling points (for example, 3) are used to achieve coarse calibration of other pixels, and the extended PSF model of coarse calibration of pixel inhomogeneity is obtained. Figure 3 As shown in Figure 2, after coarse calibration, the PSF (pixel response) of different pixels is close to the benchmark PSF model of fine calibration, so the extended PSF model can be approximated as an accurate PSF model. This method reduces the calibration cost from sub-pixel sampling points (for example, ) is reduced to 3, which reduces the calibration cost while ensuring the accuracy of PSF and expands the applicable area of ​​the accurate PSF model, making it suitable for practical engineering applications.

[0056] In combination with the above embodiments, in one embodiment, the embodiments of the present application further provide a method for wide-area high-precision positioning of point targets by combining fine and coarse calibration of an imaging model. In this method, the above step S130 of "locating the wide-area point target image acquired by the image detector using the extended PSF model and the maximum likelihood method to obtain a positioning result" specifically includes sub-steps S130-1 to S130-3: Step S130 - 1 : extracting an imaging area from the wide-area point target image, and removing the image background value of the imaging area to obtain a pixel response matrix of the imaging area.

[0057] Wide-area point target images typically refer to large-scale images containing multiple point targets (such as stars or microscopic fluorescent markers). Because actual point targets only occupy a portion of a wide-area point target image (e.g., the image spot of a star in a star sensor), it is necessary to extract the imaging area from the wide-area point target image. Specifically, methods such as threshold segmentation, edge detection, or template matching can be used to extract the effective imaging area of ​​the target, thereby eliminating redundant areas with no signal or interference and narrowing the scope for subsequent processing.

[0058] Image background values ​​include non-target signals such as detector dark current, ambient stray light, and electronic noise, which can affect positioning accuracy. Therefore, it is necessary to remove the image background value from the imaging area. This can be achieved by calculating the average background value of adjacent pixels outside the imaging area, or by extracting low-frequency background components through low-pass filtering. This estimated background value is then subtracted from the original grayscale value of each pixel in the imaging area to eliminate baseline offset and highlight the true response of the target signal.

[0059] After removing the image background value, the remaining value of each pixel in the imaging region (ROI) represents the response intensity of the detector to the point target light signal, forming a two-dimensional matrix, namely the imaging region pixel response matrix , Indicates the third pixel in the imaging area The pixel response matrix of the imaging area eliminates non-target interference and directly reflects the energy distribution characteristics of the point target. Therefore, the target position can be located based on the pixel response matrix of the imaging area.

[0060] Step S130 - 2 : establishing a joint probability distribution of the pixel response matrix of the imaging area according to the extended PSF model.

[0061] Specifically, the number of photons detected during the pixel response process is determined to obey the Poisson distribution, and the mean of the Poisson distribution is determined based on the extended PSF model; based on the mean of the Poisson distribution, the pixel response probability density function of the third pixel is determined, and the third pixel is any pixel in the imaging area; the pixel response probability density functions of all pixels in the imaging area are multiplied to obtain the joint probability distribution.

[0062] Among them, the number of photons detected during the pixel response process follows the Poisson distribution, that is, It obeys the Poisson distribution, and the mean of the Poisson distribution is , where K is the pixel gain, i.e. the gain coefficient for converting the number of photons into pixel values. Specifically, K can be determined by the photon transfer method or by consulting a manual. Therefore, the pixel response probability density function is It can be expressed as:

[0063] in, Indicates the position of the image spot.

[0064] Multiply the pixel response probability density functions of all pixels in the imaging area to obtain the joint probability distribution It can be expressed as:

[0065] Step S130 - 3 : Based on the maximum likelihood method and according to the joint probability distribution, the image spot position with the maximum probability of the pixel response matrix in the imaging area appearing is calculated as the positioning result.

[0066] In the embodiment of the present application, the image spot position with the maximum probability of the pixel response matrix appearing in the imaging area is taken as the target positioning result. In order to maximize the probability of the pixel response matrix appearing in the imaging area, that is, , solve it by constructing a cost function, and use the solution as the positioning result.

[0067] Specifically, based on the maximum likelihood method and according to the joint probability distribution, calculating the image spot position that maximizes the probability of the pixel response matrix of the imaging area appearing as the positioning result, including: constructing a cost function according to the joint probability distribution; minimizing the cost function using an iterative Newton-Raphson method, and obtaining the positioning result when an iteration end condition is met; Take the negative natural logarithm of the joint probability distribution as the cost function, that is, the cost function It can be expressed as:

[0068] The item in the cost function related to the image spot position Expressed as:

[0069] Where i and j represent the row and column of the pixel respectively, K represents the pixel gain, Represents pixels The relative response intercept relative to the first pixel, Indicates the distance between the image spot position and the second pixel center The pixel response value, Indicates the actual pixel response value of the second pixel collected.

[0070] The initial position of the image spot can be calculated by the centroid method, and the process of minimizing the cost function using the iterative Newton-Raphson method is as follows:

[0071]

[0072] Among them, H and J are cost functions The Jacobian matrix and Hessian matrix are specifically:

[0073]

[0074] in,

[0075] Specifically, the iteration termination condition can be that the iterative change is less than a change threshold, or the number of iterations is greater than a number threshold. That is, when the change in the target position during an iteration is less than the change threshold, or the number of iterations is greater than the number threshold, the iteration ends and the high-precision positioning result of the target is output. In some embodiments, the iterative change threshold is set to 0.001 pixels, and the number threshold is set to 10 times. In this embodiment, the algorithm typically converges after 2-4 iterations.

[0076] Through the above implementation process, the wide-area point target images collected by the image detector are positioned by extending the PSF model and the maximum likelihood method, making full use of the distribution characteristics of random noise to achieve point target positioning measurement close to the theoretical accuracy limit, thereby improving the accuracy of point target positioning.

[0077] The following describes a method for wide-area high-precision positioning of point targets using a combination of fine and coarse calibration of an imaging model according to an embodiment of the present application, with reference to a specific embodiment. Figure 4 As shown, the method for wide-area high-precision positioning of point targets by combining fine and coarse calibration of the imaging model may include steps S410 to S490: Step S410: capturing multiple frames of point target images at different sub-pixel positions within a target window centered on the first pixel.

[0078] Step S420: Based on the multi-frame point target images collected at each sub-pixel position, the pixel response value corresponding to each sub-pixel position is calculated to obtain the imaging PSF sampling matrix of the first pixel, where each element in the imaging PSF sampling matrix represents the pixel response value corresponding to different positions of the image spot center from the center of the first pixel.

[0079] Step S430: performing interpolation processing on the imaging PSF sampling matrix to obtain a reference PSF model.

[0080] Step S440: moving the point target within the target window centered at the second pixel, and collecting imaging data at multiple target positions to obtain imaging data of the second pixel.

[0081] Step S450: Mapping the reference PSF model to a second pixel, and predicting a pixel response of the second pixel according to the reference PSF model to obtain a predicted pixel response value of the second pixel.

[0082] Step S460: fitting the predicted pixel response value of the second pixel and the imaging data of the second pixel using the least square method to obtain an extended PSF model.

[0083] Step S470: extracting an imaging area from the wide-area point target image, and removing the image background value of the imaging area to obtain a pixel response matrix of the imaging area.

[0084] Step S480: establishing a joint probability distribution of the pixel response matrix of the imaging area according to the extended PSF model.

[0085] Step S490: Based on the maximum likelihood method and according to the joint probability distribution, the image spot position with the maximum probability of the pixel response matrix in the imaging area appearing is calculated as the positioning result.

[0086] In the embodiments of this application, a precisely calibrated baseline PSF model is used as the core of the system model. The maximum likelihood method fully utilizes the distribution characteristics of random noise to achieve point target positioning measurement close to the theoretical accuracy limit, significantly improving accuracy compared to traditional centroid methods and Gaussian fitting methods. Furthermore, using a coarsely calibrated extended PSF model as an approximation greatly simplifies the PSF calibration process, eliminating the need for additional calibration equipment such as an integrating sphere. This also reduces the amount of data in the PSF model, facilitating model writing and making it suitable for practical engineering applications.

[0087] Furthermore, in order to better illustrate the method implemented in this application, the wide-area high-precision positioning method of point targets using the combination of fine and coarse calibration of the imaging model implemented in this application is compared with the maximum likelihood method using single-pixel fine calibration and the traditional centroid positioning method. The error results are shown in the figure below. Figure 5 As shown. It can be seen that the average centering error pixel of the traditional centroid positioning method is 0.0501, the average centering error pixel of the maximum likelihood method with single-pixel precision calibration is 0.0083, and the average centering error pixel of the wide-area high-precision positioning method for point targets that combines fine-coarse calibration of the imaging model implemented in this application is 0.0040. Therefore, the wide-area high-precision positioning method for point targets that combines fine-coarse calibration of the imaging model provided in the embodiment of this application can significantly improve the positioning accuracy of point targets compared with existing positioning methods.

[0088] Based on the same technical concept, the embodiment of the present application also provides a point target wide-area high-precision positioning device combining fine and coarse calibration of an imaging model, referring to Figure 6 As shown, Figure 6 : This is a schematic diagram of the structure of a point target wide-area high-precision positioning device that combines fine and coarse calibration of an imaging model provided in an embodiment of the present application, the device comprising: a calibration module 610 configured to acquire point target images at different sub-pixel positions and calibrate the point spread function (PSF) of a first pixel on an image detector to obtain a reference PSF model; wherein the first pixel is any pixel on the image detector; A fitting module 620 is configured to map the baseline PSF model to a second pixel and fit the second pixel's imaging data to obtain an extended PSF model. The second pixel is any pixel on the image detector other than the first pixel, and the imaging data is response values ​​of the second pixel acquired when the imaging spot center is located at multiple different positions within an image region near the center of the second pixel. The extended PSF model represents pixel response values ​​corresponding to different positions of the spot center from the pixel center. The positioning module 630 is used to locate the wide-area point target image collected by the image detector by using the extended PSF model and the maximum likelihood method to obtain a positioning result.

[0089] In an optional embodiment, the calibration module includes: An image acquisition module, configured to acquire multiple frames of point target images at different sub-pixel positions within a target window centered on the first pixel; a response calculation module, configured to calculate a pixel response value corresponding to each sub-pixel position based on the multiple frames of point target images acquired at each sub-pixel position, and obtain an imaging PSF sampling matrix of the first pixel, wherein each element in the imaging PSF sampling matrix represents a pixel response value corresponding to a different position of the image spot center from the center of the first pixel; The interpolation processing module is used to perform interpolation processing on the imaging PSF sampling matrix to obtain a reference PSF model.

[0090] In an optional embodiment, the image acquisition module is specifically used to use a motion actuator to move the image detector to form a relative micro-motion between the point target and the image detector, and control the image spot of the point target to perform sub-pixel micro-displacement within the target window centered on the first pixel; perform multi-frame point target image sampling at each sub-pixel position, and obtain multi-frame point target images collected at different sub-pixel positions within the first pixel.

[0091] In an optional embodiment, the fitting module includes: a data acquisition module, configured to move a point target image spot within a target window centered on the second pixel, and acquire imaging data at a plurality of target positions to obtain imaging data of the second pixel; a response prediction module, configured to map the reference PSF model to a second pixel, and predict a pixel response of the second pixel based on the reference PSF model to obtain a predicted pixel response value of the second pixel; The data fitting module is used to fit the predicted pixel response value of the second pixel and the imaging data of the second pixel using the least square method to obtain an extended PSF model.

[0092] In an optional embodiment, the predicted pixel response value of the second pixel includes predicted pixel response values ​​corresponding to multiple target positions; the response prediction module is specifically used to map the benchmark PSF model to the second pixel, and determine the benchmark positions corresponding to the multiple target positions from the benchmark PSF model; and use the pixel response value corresponding to the benchmark position in the benchmark PSF model as the predicted pixel response value corresponding to the target position in the second pixel.

[0093] In an optional embodiment, the positioning module includes: A region extraction module is used to extract an imaging region from the wide-area point target image and remove the image background value of the imaging region to obtain a pixel response matrix of the imaging region; A distribution establishment module, configured to establish a joint probability distribution of a pixel response matrix of the imaging area according to the extended PSF model; The position calculation module is used to calculate the image spot position with the maximum probability of the pixel response matrix of the imaging area appearing as the positioning result based on the maximum likelihood method and the joint probability distribution.

[0094] In an optional embodiment, the distribution establishment module is specifically used to determine the number of photons detected during the pixel response process as obeying a Poisson distribution, and determine the mean of the Poisson distribution based on the extended PSF model; determine the pixel response probability density function of a third pixel based on the mean of the Poisson distribution, where the third pixel is any pixel in the imaging area; and multiply the pixel response probability density functions of all pixels in the imaging area to obtain the joint probability distribution.

[0095] The present application also provides an electronic device, Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, the electronic device 700 includes: a memory 710 and a processor 720. The memory 710 and the processor 720 are connected via a bus communication. A computer program is stored in the memory 710, and the computer program can be run on the processor 720 to implement the steps of the method for wide-area high-precision positioning of point targets combining fine and coarse calibration of the imaging model as described in the embodiment of the present application.

[0096] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for wide-area high-precision positioning of point targets combining fine and coarse calibration of the imaging model described in the embodiment of the present application are implemented.

[0097] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for wide-area high-precision positioning of point targets combining fine and coarse calibration of an imaging model as described in the embodiment of the present application.

[0098] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0099] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods and devices according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0102] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0103] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0104] The above is a detailed introduction to the method, device and equipment for wide-area high-precision positioning of point targets that combines fine and coarse calibration of an imaging model provided by this application. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method of this application and its core idea; at the same time, for general technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.

Claims

1. A method for wide-area high-precision positioning of point targets combining fine and coarse calibration of an imaging model, characterized in that: The method comprises: Acquire point target images at different sub-pixel positions, calibrate the point spread function (PSF) of the first pixel on the image detector, and obtain a reference PSF model; wherein the first pixel is any pixel on the image detector; Mapping the reference PSF model to a second pixel and fitting it with imaging data of the second pixel to obtain an extended PSF model; wherein the second pixel is any pixel other than the first pixel on the image detector, the imaging data is response values ​​of the second pixel acquired when the center of the imaging spot is located at multiple different positions within an image region near the center of the second pixel, and the extended PSF model represents pixel response values ​​corresponding to different positions of the image spot center from the pixel center; The wide-area point target image collected by the image detector is positioned by using the extended PSF model and the maximum likelihood method to obtain a positioning result.

2. The method for wide-area high-precision positioning of point targets using a combination of fine and coarse calibration of an imaging model according to claim 1, characterized in that: Collect point target images at different sub-pixel positions, calibrate the point spread function (PSF) of the first pixel on the image detector, and obtain a benchmark PSF model, including: collecting multiple frames of point target images at different sub-pixel positions within a target window centered on the first pixel; Calculating a pixel response value corresponding to each sub-pixel position based on the multiple frames of point target images collected at each sub-pixel position to obtain an imaging PSF sampling matrix for the first pixel, wherein each element in the imaging PSF sampling matrix represents a pixel response value corresponding to a different position of the image spot center from the center of the first pixel; An interpolation process is performed on the imaging PSF sampling matrix to obtain a reference PSF model.

3. The method for wide-area high-precision positioning of point targets using a combination of fine and coarse calibration of an imaging model according to claim 2, characterized in that: Acquiring multiple frames of point target images at different sub-pixel positions within a target window centered on the first pixel, respectively, including: Using a motion actuator to move the image detector, thereby forming a relative micro-motion between the point target and the image detector, and controlling the image spot of the point target to perform a sub-pixel micro-displacement within a target window centered on the first pixel; Multi-frame point target image sampling is performed at each sub-pixel position to obtain multi-frame point target images collected at different sub-pixel positions within the first pixel.

4. The method for wide-area high-precision positioning of point targets using a combination of fine and coarse calibration of an imaging model according to any one of claims 1 to 3, characterized in that: Mapping the reference PSF model to a second pixel and fitting it with imaging data of the second pixel to obtain an extended PSF model, including: Moving the point target image spot within a target window centered on the second pixel, and collecting imaging data at multiple target positions to obtain imaging data of the second pixel; Mapping the reference PSF model to a second pixel, and predicting a pixel response of the second pixel based on the reference PSF model to obtain a predicted pixel response value of the second pixel; The predicted pixel response value of the second pixel and the imaging data of the second pixel are fitted using a least squares method to obtain an extended PSF model.

5. The method for wide-area high-precision positioning of point targets using a combination of fine and coarse calibration of an imaging model according to claim 4, characterized in that: The predicted pixel response value of the second pixel includes predicted pixel response values ​​corresponding to a plurality of target positions; mapping the benchmark PSF model to the second pixel, and predicting the pixel response value of the second pixel according to the benchmark PSF model to obtain the predicted pixel response value of the second pixel, including: Mapping the reference PSF model to the second pixel, and determining reference positions corresponding to the plurality of target positions from the reference PSF model; The pixel response value corresponding to the reference position in the reference PSF model is used as the predicted pixel response value corresponding to the target position in the second pixel.

6. The method for wide-area high-precision positioning of point targets using a combination of fine and coarse calibration of an imaging model according to claim 1, characterized in that: Positioning the wide-area point target image collected by the image detector using the extended PSF model and the maximum likelihood method to obtain a positioning result, including: Extracting an imaging area from the wide-area point target image and removing an image background value of the imaging area to obtain a pixel response matrix of the imaging area; Establishing a joint probability distribution of a pixel response matrix of the imaging area according to the extended PSF model; Based on the maximum likelihood method and according to the joint probability distribution, the image spot position with the maximum probability of occurrence of the pixel response matrix in the imaging area is calculated as the positioning result.

7. The method for wide-area high-precision positioning of point targets using a combination of fine and coarse calibration of an imaging model according to claim 6, characterized in that: Establishing a joint probability distribution of the pixel response matrix of the imaging area according to the extended PSF model includes: Determining the number of photons detected during the pixel response process to obey a Poisson distribution, and determining a mean of the Poisson distribution according to the extended PSF model; determining a pixel response probability density function of a third pixel according to the mean of the Poisson distribution, where the third pixel is any pixel in the imaging area; The pixel response probability density functions of all pixels in the imaging area are multiplied to obtain the joint probability distribution.

8. The method for wide-area high-precision positioning of point targets using a combination of fine and coarse calibration of an imaging model according to claim 6, characterized in that: Based on the maximum likelihood method and according to the joint probability distribution, the image spot position with the maximum probability of the pixel response matrix in the imaging area appearing is calculated as the positioning result, including: Constructing a cost function according to the joint probability distribution; The cost function is minimized using an iterative Newton-Raphson method, and a positioning result is obtained when an iteration end condition is met; The item in the cost function related to the image spot position Expressed as: ， Where i and j represent the row and column of the pixel respectively, K represents the pixel gain, Represents pixels The relative response intercept relative to the first pixel, Indicates the distance between the image spot position and the second pixel center The pixel response value, Indicates the actual pixel response value of the second pixel collected.

9. A point target wide-area high-precision positioning device combining fine and coarse calibration of an imaging model, characterized in that: The device comprises: a calibration module, configured to acquire point target images at different sub-pixel positions and calibrate the point spread function (PSF) of a first pixel on an image detector to obtain a reference PSF model; wherein the first pixel is any pixel on the image detector; a fitting module, configured to map the reference PSF model to a second pixel and fit the second pixel's imaging data to obtain an extended PSF model; wherein the second pixel is any pixel on the image detector other than the first pixel, the imaging data is response values ​​of the second pixel acquired when the imaging spot center is located at a plurality of different positions within an image region near the center of the second pixel, and the extended PSF model represents pixel response values ​​corresponding to different positions of the spot center from the pixel center; The positioning module is used to locate the wide-area point target image collected by the image detector by using the extended PSF model and the maximum likelihood method to obtain a positioning result.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for wide-area high-precision positioning of point targets combining fine and coarse calibration of an imaging model as described in any one of claims 1 to 8 are implemented.

Images