Image blur correction method and device, electronic equipment and medium

Abstract

The present application belongs to the technical field of image processing, and provides an image blur correction method and device, electronic equipment and medium, comprising: based on a blurred image, projecting the image and a standard map to establish a Gauss-Krueger projection model, obtaining auxiliary parameters, calculating the horizontal coordinate and vertical coordinate in the projection plane rectangular coordinate system based on the auxiliary parameters, converting the pixel points into plane rectangular coordinate system coordinates, and marking the converted coordinates of the pixel points as converted coordinates; if the converted coordinates and the projection coordinates are inconsistent in the same plane rectangular coordinate system, a sampling signal is generated; based on the sampling signal, the image is resampled using a bilinear interpolation method; if the pixel value of the pixel point of the sampled image is less than the pixel value of the pixel point of the original image, the pixel point is marked as a deteriorated pixel point; based on the deteriorated pixel point, a deterioration expression value is obtained; based on the adjustment signal, error data is obtained; and the deteriorated coordinates are adjusted according to the error data.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, specifically to image blur correction methods, devices, electronic devices, and media. Background Technology

[0002] Image blur is a common problem that can be caused by a variety of factors, such as lens shake, motion blur, and defocus. These factors can lead to the loss of image details, affecting image sharpness and visual quality. Traditional image blur correction methods usually rely on complex algorithms and a lot of computing resources, and their effects are limited. Therefore, a more efficient and accurate image blur correction method is needed to improve image quality and user experience.

[0003] A Chinese patent application with publication number CN110660034A discloses an image correction method, apparatus, and electronic device, including: This invention provides an image correction method, apparatus, and electronic device, relating to the field of image processing technology. The method includes: acquiring an image to be corrected and device parameters of the acquisition device for the image to be corrected; detecting a target object in the image to be corrected to obtain object information; the object information including the size and / or position of the target object in the image to be corrected; and performing correction processing on the image to be corrected based on the object information and the device parameters.

[0004] However, the aforementioned existing technologies detect objects in the image by acquiring and recognizing parameters of the image to be corrected, but lack analysis of the image to be corrected itself and the image pixels, thus failing to guarantee the correction effect and visual quality of the image.

[0005] To this end, the present invention provides an image blur correction method, apparatus, electronic device, and medium. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by the present invention to solve its technical problem is: to acquire a blurred image, to uniformly select multiple pixels within the image range to ensure coverage of all areas, and to establish a Gauss-Kruger projection model;

[0008] Based on the Gauss-Kruger projection model, auxiliary parameters are obtained using the WGS84 standard ellipsoid model as a reference. The coordinate transformation formula of the Gauss-Kruger projection is then calculated based on the auxiliary parameters to obtain the abscissa and ordinate in the Cartesian coordinate system.

[0009] Based on the Gauss-Kruger projection coordinate transformation relationship, the pixel points are transformed into Cartesian coordinates, and the transformed coordinates of the pixel points are marked as transformed coordinates. Based on the comparison relationship between the transformed coordinates and the projected coordinates, if the transformed coordinates and the projected coordinates are inconsistent in the same Cartesian coordinate system, the image is resampled to generate a sampled signal.

[0010] Based on the sampled signal, the image is resampled using bilinear interpolation to obtain a sampled image. The difference between the pixel value of the sampled image and the corresponding pixel value in the original image is calculated to obtain the sampled pixel difference. The sampled pixel difference is compared with a sampled pixel difference threshold. If the sampled pixel difference is greater than the sampled pixel difference threshold, the pixel is marked as a deteriorated pixel. Based on the deteriorated pixels, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. Based on the proportion of deterioration quantity and the proportion of deterioration degree, the deterioration performance value is obtained.

[0011] Based on the adjustment signal, a fitting curve for the transformed coordinates and projected coordinates is established, error data is obtained, and the deteriorated coordinates are adjusted according to the error data;

[0012] A further technical solution of the present invention is: the percentage of deterioration quantity is marked as SL, and the percentage of deterioration degree is marked as CD;

[0013] Through formula The deterioration performance value BX is calculated, where s1 and s2 are preset proportional coefficients;

[0014] A further technical solution of the present invention is as follows: the method for obtaining the percentage of deteriorated quantities is:

[0015] Compare all optimized pixel values ​​with the original pixel values;

[0016] If the optimized pixel value is greater than or equal to the original pixel value, then this pixel is marked as an improved pixel.

[0017] If the optimized pixel value is less than the original pixel value, then this pixel is marked as a deteriorated pixel.

[0018] The number of degraded pixels is counted, and the ratio of the number of degraded pixels to the total number of pixels is calculated to obtain the percentage of degraded pixels.

[0019] A further technical solution of the present invention is as follows: the method for obtaining the percentage of deterioration is:

[0020] The mean value of degraded pixels is calculated by summing all the degraded pixel values ​​and taking the average.

[0021] The difference between the mean value of the degraded pixels and the value of the degraded pixels is calculated, and the absolute value is taken to obtain the degraded pixel deviation value.

[0022] The difference between the deteriorated pixel deviation value and the deteriorated pixel mean value is calculated and the absolute value is taken to obtain the deteriorated pixel mean deviation value.

[0023] The average value of the original pixels is obtained by summing all the original pixel values ​​and taking the mean value.

[0024] The ratio of the average deviation value of the deteriorated pixels to the average value of the original pixels is calculated to obtain the percentage of deterioration.

[0025] A further technical solution of the present invention is: the proportion of the number of overlapping stage curves is SV, and the proportion of the remaining area is marked as IP;

[0026] Through formula The error characterization value is calculated, where a1 and a2 are both preset proportional coefficients;

[0027] A further technical solution of the present invention is as follows: the method for obtaining the proportion of overlapping stage curves is as follows:

[0028] The straight line containing the abscissa of the coordinate system is divided into several sub-stages, and the number of overlapping stage curves is measured. The overlapping stage curves represent the stage curves that overlap between the transformed coordinate fitting curve and the projected coordinate fitting curve.

[0029] The ratio of the number of overlapping stage curves to the total number of stage curves is calculated, and the percentage of overlapping stage curves is obtained.

[0030] A further technical solution of the present invention is as follows: the method for obtaining the remaining area percentage is:

[0031] Extend the endpoints of the two curves toward the X-axis to obtain the figure enclosed by the curves and the X-axis;

[0032] The areas of the figures enclosed by the two curves and the X-axis, as well as the areas of the overlapping parts of the two curves, are calculated using mathematical methods. The resulting areas are the overlapping area, the area of ​​the transformed coordinate curve, and the area of ​​the projected coordinate curve.

[0033] The difference between the area of ​​the transformed coordinate curve and the area of ​​the curve that coincides with the curve is calculated, and the absolute value is taken to obtain the remaining area after transformation.

[0034] The difference between the area of ​​the projected coordinate curve and the area of ​​the curve that coincides with the curve is calculated, and the absolute value is taken to obtain the remaining projected area.

[0035] The remaining area is obtained by summing the transformed remaining area and the projected remaining area;

[0036] The ratio of the total remaining area to the area of ​​the projected coordinate curve is calculated, and the percentage of the remaining area is obtained.

[0037] A further technical solution of the present invention is: an image blur correction device, comprising:

[0038] Coordinate acquisition module: Based on the fuzzy image, the image is projected onto a standard map, and pixels that are evenly distributed and cover the entire image are selected to establish a Gauss-Kruger projection model.

[0039] Model building module: Based on the Gauss-Kruger projection model, auxiliary parameters are obtained using the WGS84 standard ellipsoid model as a reference. Based on the auxiliary parameters, the coordinate transformation formula of the Gauss-Kruger projection is calculated to obtain the abscissa and ordinate in the plane rectangular coordinate system.

[0040] Signal acquisition module: Based on the Gauss-Kruger projection coordinate transformation relationship, the pixel points are transformed into Cartesian coordinates and the transformed coordinates of the pixel points are marked as transformed coordinates. Based on the comparison relationship between the transformed coordinates and the projected coordinates, if the transformed coordinates and the projected coordinates are inconsistent on the same Cartesian coordinate system, the image is resampled to generate a sampled signal.

[0041] Sampling signal analysis module: Based on the sampling signal, the image is resampled using bilinear interpolation to obtain a sampled image. The difference between the pixel value of the sampled image and the corresponding pixel value in the original image is calculated to obtain the sampled pixel difference. The sampled pixel difference is compared with the sampled pixel difference threshold. If the sampled pixel difference is greater than the sampled pixel difference threshold, the pixel is marked as a deteriorated pixel. Based on the deteriorated pixels, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. Based on the proportion of deterioration quantity and the proportion of deterioration degree, the deterioration performance value is obtained.

[0042] Image adjustment module: Based on the adjustment signal, establish a fitting curve for transformed coordinates and projected coordinates, obtain error data, and adjust the deteriorated coordinates according to the error data;

[0043] An electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0044] Memory, used to store computer programs;

[0045] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method steps.

[0046] The beneficial effects of this invention are as follows:

[0047] 1. By projecting the blurred image using the Gauss-Kruger projection model, the blurred image can be corrected in the form of coordinates, which helps to improve the accuracy of image blur correction technology.

[0048] 2. Based on the sampled signal, the image is resampled using bilinear interpolation to obtain an optimized image. The original pixel value is compared with the optimized pixel value. If the optimized pixel value is less than the original pixel value, this pixel is marked as a deteriorated pixel. Based on the deteriorated pixels, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. The deterioration performance value is obtained based on the proportion of deterioration quantity and the proportion of deterioration degree. The deterioration performance value is compared with a threshold to obtain an adjustment signal. Based on the adjustment signal, a fitting curve of transformation coordinates and projection coordinates is established to obtain error data. The deterioration coordinates are adjusted according to the error data. This invention effectively ensures the correction effect of the blurred image and improves the image appearance by processing the deteriorated pixel value and correcting the blurred image through coordinate transformation. Attached Figure Description

[0049] The invention will now be further described with reference to the accompanying drawings.

[0050] Figure 1 This is a flowchart of the steps in Embodiment 1 of the present invention;

[0051] Figure 2 This is a flowchart of the steps in Embodiment 2 of the present invention;

[0052] Figure 3 This is a flowchart of the apparatus in Embodiment 3 of the present invention;

[0053] Figure 4 This is a diagram of an electronic device according to Embodiment 4 of the present invention. Detailed Implementation

[0054] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0055] Example 1

[0056] like Figure 1 The image blur correction method described in the embodiment of the present invention includes:

[0057] Step 1: Obtain a blurred image, select multiple pixels evenly within the image range to ensure coverage of all areas, and establish a Gauss-Kruger projection model;

[0058] By using geographic information system data, accurate maps, and reliable geographic information sources, we obtain the GPS coordinates and terrain features of all pixels, establish a Gauss-Kruger projection model, and mark the positions of the pixels in the Gauss-Kruger projection coordinate system.

[0059] Among them, the acquisition of terrain features is to facilitate the establishment of the Gauss-Kruger projection model;

[0060] Step 2: Based on the Gauss-Kruger projection model, obtain auxiliary parameters using the WGS84 standard ellipsoid model as a reference, and calculate the coordinate transformation formula of the Gauss-Kruger projection based on the auxiliary parameters to obtain the x-coordinate and y-coordinate in the plane rectangular coordinate system.

[0061] Among them, the auxiliary parameters include the radius of curvature of the trochanteric circle, the arc length of the meridian, and the value of the arc length of the meridian on the central meridian;

[0062] Mark the radius of curvature of the zonal circle as N, the arc length of the meridian as M, and the distance of the arc length of the meridian from the central meridian as M0;

[0063] Among them, the radius of curvature N of the zonal circle is the radius of curvature of the zonal circle (i.e., the circle perpendicular to the latitude circle at that point) at a certain point on the Earth's ellipsoid, and the meridian arc length M is the meridian length from the equator of the ellipsoid to a certain latitude B.

[0064] Through formula The radius of curvature N of the zonal circle is calculated, where a represents the semi-major axis, f represents the flattening, and B represents the latitude of the point.

[0065] It should be noted that the semi-major axis 'a' and the flattening f are obtained from the standard ellipsoid model WGS84;

[0066] Through formula The meridian arc length M is calculated, where m0, m2, m4, m6, ... are coefficients related to the ellipsoid parameters. These coefficients can be calculated from the semi-major axis a and flattening f of the ellipsoid. B represents the dimension of the point. These coefficients are known based on commonly used ellipsoids such as the WGS84 ellipsoid.

[0067] In the WGS84 standard ellipsoid model, the value of the meridian arc length on the central meridian is a known constant;

[0068] Through formula Calculate the value of the abscissa in the Cartesian coordinate system;

[0069] Through formula Calculate the value of the ordinate in the Cartesian coordinate system;

[0070] Where X and Y represent the abscissa and ordinate in the Cartesian coordinate system, respectively, k0 is the scale factor, usually taken as 1; B and L are the latitude and longitude of the point, respectively, and L0 is the longitude of the central meridian.

[0071] Step 3: Based on the Gauss-Kruger projection coordinate transformation relationship, the pixel points are transformed into Cartesian coordinates, and the transformed coordinates of the pixel points are marked as transformed coordinates. Based on the comparison relationship between the transformed coordinates and the projected coordinates, if the transformed coordinates and the projected coordinates are inconsistent on the same Cartesian coordinate system, the image is resampled to generate a sampled signal.

[0072] It should be noted that the meaning of projected coordinates is: the coordinates of a pixel projected onto a Cartesian coordinate system;

[0073] If the image is a rectangle with its bottom left corner being the origin (0, 0), the positive x-axis is to the right, and the positive y-axis is upward;

[0074] Images are typically stored in computers as two-dimensional matrices. Here, we use two-dimensional matrices to solve for the coordinate transformation of pixels.

[0075] For any pixel, mark it in the m-th row and n-th column of the two-dimensional matrix, that is, the position of the pixel in the two-dimensional matrix is... ;

[0076] The x-coordinate of a pixel in the coordinate system is the x-coordinate of the pixel in the two-dimensional matrix, that is:

[0077]

[0078] The ordinate of a pixel in the coordinate system is the x-coordinate of the pixel in the two-dimensional matrix, that is:

[0079]

[0080] Transform the coordinates of all pixels With projected coordinates Compare;

[0081] If the transformed coordinates and projected coordinates of a pixel are consistent in the same Cartesian coordinate system, no processing is required.

[0082] If the transformed coordinates and projected coordinates of a pixel do not match in the same Cartesian coordinate system, the image is resampled to generate a sampled signal.

[0083] It should be noted that image resampling means increasing the resolution of the image, so that the blurred image has more pixel information during correction, which helps to improve the quality of the final image.

[0084] The technical solution of this invention is as follows: By using the Gauss-Kruger projection model, the blurred image is projected, and the blurred image is corrected in the form of coordinates, which helps to improve the accuracy of image blur correction technology.

[0085] Example 2

[0086] like Figure 2 As shown in Embodiment 1, the image blur correction method of this invention includes:

[0087] Step 4: Based on the sampled signal, the image is resampled using bilinear interpolation to obtain a sampled image. The difference between the pixel value of the sampled image and the corresponding pixel value in the original image is calculated to obtain the sampled pixel difference. The sampled pixel difference is compared with the sampled pixel difference threshold. If the sampled pixel difference is greater than the sampled pixel difference threshold, the pixel is marked as a deteriorated pixel. Based on the deteriorated pixels, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. Based on the proportion of deterioration quantity and the proportion of deterioration degree, the deterioration performance value is obtained.

[0088] In some embodiments, the pixel value of a sampled image pixel is calculated by bilinear interpolation, the difference between the sampled image pixel and the pixel value of the same pixel in the original image is calculated, and the absolute value is taken to obtain the sampled pixel difference of that pixel.

[0089] Compare the sampled pixel difference with the sampled pixel difference threshold;

[0090] If the difference between sampled pixels is greater than the threshold for the difference between sampled pixels, then the pixel is marked as a deteriorated pixel.

[0091] If the difference between sampled pixels is less than or equal to the threshold value for the difference between sampled pixels, then the pixel is marked as an improved pixel.

[0092] Based on the degraded pixels, the number of degraded pixels is counted, and the ratio of the number of degraded pixels to the total number of pixels is calculated to obtain the proportion of degraded pixels.

[0093] Sum the differences of all sampled pixels and take the average value to obtain the average difference of sampled pixels;

[0094] The average difference between the sampled pixels of the deteriorated pixels is obtained by summing the differences and processing them.

[0095] The ratio of the average difference of degraded pixels to the average difference of sampled pixels is used to obtain the percentage of degraded pixels.

[0096] The percentage of deterioration is labeled SL, and the percentage of deterioration severity is labeled CD.

[0097] Through formula The deterioration performance value BX is calculated, where s1 and s2 are preset proportional coefficients;

[0098] Compare the degraded pixel performance value BX with the degraded pixel performance threshold;

[0099] If the degraded pixel performance value BX is less than or equal to the degraded pixel performance threshold, no processing is performed;

[0100] If the degraded pixel performance value BX is greater than the degraded pixel performance threshold, an adjustment signal is generated;

[0101] Step 5: Based on the adjustment signal, establish the fitting curves of transformed coordinates and projected coordinates, obtain error data, and adjust the deteriorated coordinates according to the error data;

[0102] It should be noted that the error data includes the percentage of remaining area and the percentage of overlapping curves.

[0103] Here, the meaning of the deterioration coordinates is: the coordinates of the deteriorated pixel;

[0104] In some embodiments, the transformed coordinates and projected coordinates are marked on the same coordinate system, and the marked points are connected to obtain a fitting curve of the transformed coordinates and projected coordinates;

[0105] The straight line containing the abscissa of the coordinate system is divided into several sub-stages, and the number of overlapping stage curves is measured. The overlapping stage curves represent the stage curves that overlap between the transformed coordinate fitting curve and the projected coordinate fitting curve.

[0106] The ratio of the number of overlapping stage curves to the total number of stage curves is calculated, and the percentage of overlapping stage curves is obtained.

[0107] Extend the endpoints of the two curves toward the X-axis to obtain the figure enclosed by the curves and the X-axis;

[0108] The areas of the figures enclosed by the two curves and the X-axis, as well as the areas of the overlapping parts of the two curves, are calculated using mathematical methods. The resulting areas are the overlapping area, the area of ​​the transformed coordinate curve, and the area of ​​the projected coordinate curve.

[0109] The difference between the area of ​​the transformed coordinate curve and the area of ​​the curve that coincides with the curve is calculated, and the absolute value is taken to obtain the remaining area after transformation.

[0110] The difference between the area of ​​the projected coordinate curve and the area of ​​the curve that coincides with the curve is calculated, and the absolute value is taken to obtain the remaining projected area.

[0111] The remaining area is obtained by summing the transformed remaining area and the projected remaining area;

[0112] The ratio of the total remaining area to the area of ​​the projected coordinate curve is calculated, and the percentage of the remaining area is obtained.

[0113] The percentage of overlapping curves is SV, and the percentage of remaining area is marked as IP.

[0114] Through formula The error characterization value is calculated, where a1 and a2 are both preset proportional coefficients;

[0115] Compare the error characterization value with the error characterization threshold;

[0116] If the error characterization value is greater than or equal to the error characterization threshold, it indicates that the image blurring is related to improper coordinate transformation. Correct the image based on the projected coordinates to make the blurry image clear.

[0117] If the error characterization value is less than the error characterization threshold, it indicates that the image blurring is unrelated to the transformed coordinates.

[0118] The technical solution of this invention is as follows: Based on the sampling signal, the image is resampled using bilinear interpolation to obtain an optimized image. The original pixel value is compared with the optimized pixel value. If the optimized pixel value is less than the original pixel value, this pixel is marked as a deteriorated pixel. Based on the deteriorated pixel, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. The deterioration performance value is obtained based on the proportion of deterioration quantity and the proportion of deterioration degree. The deterioration performance value is compared with a threshold to obtain an adjustment signal. Based on the adjustment signal, a fitting curve of transformation coordinates and projection coordinates is established to obtain error data. The deterioration coordinates are adjusted according to the error data. This invention effectively ensures the correction effect of the blurred image and improves the image appearance by processing the deteriorated pixel value and correcting the blurred image through coordinate transformation.

[0119] Example 3

[0120] Please see Figure 3 As shown, the present invention is an image blur correction device, comprising:

[0121] Model building module: Acquire a blurred image, select multiple pixels evenly within the image range to ensure coverage of all areas, and build a Gauss-Kruger projection model;

[0122] Coordinate acquisition module: Based on the Gauss-Kruger projection model, auxiliary parameters are obtained using the WGS84 standard ellipsoid model as a reference. Based on the auxiliary parameters, the coordinate transformation formula of the Gauss-Kruger projection is calculated to obtain the x and y coordinates in the plane rectangular coordinate system.

[0123] Signal acquisition module: Based on the Gauss-Kruger projection coordinate transformation relationship, the pixel points are transformed into Cartesian coordinates and the transformed coordinates of the pixel points are marked as transformed coordinates. Based on the comparison relationship between the transformed coordinates and the projected coordinates, if the transformed coordinates and the projected coordinates are inconsistent on the same Cartesian coordinate system, the image is resampled to generate a sampled signal.

[0124] Sampling signal analysis module: Based on the sampling signal, the image is resampled using bilinear interpolation to obtain a sampled image. The difference between the pixel value of the sampled image and the corresponding pixel value in the original image is calculated to obtain the sampled pixel difference. The sampled pixel difference is compared with the sampled pixel difference threshold. If the sampled pixel difference is greater than the sampled pixel difference threshold, the pixel is marked as a deteriorated pixel. Based on the deteriorated pixels, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. Based on the proportion of deterioration quantity and the proportion of deterioration degree, the deterioration performance value is obtained.

[0125] Image adjustment module: Based on the adjustment signal, it establishes a fitting curve for the transformed coordinates and projected coordinates, obtains error data, and adjusts the deteriorated coordinates according to the error data.

[0126] Example 4

[0127] Please see Figure 4 As shown, the present invention is an electronic device, including: a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 communicate with each other through the communication bus 304.

[0128] Memory 303 is used to store computer programs;

[0129] When processor 301 executes a program stored in memory 303, it performs the following steps:

[0130] Step 1: Obtain a blurred image, select multiple pixels evenly within the image range to ensure coverage of all areas, and establish a Gauss-Kruger projection model;

[0131] Step 2: Based on the Gauss-Kruger projection model, obtain auxiliary parameters using the WGS84 standard ellipsoid model as a reference, and calculate the coordinate transformation formula of the Gauss-Kruger projection based on the auxiliary parameters to obtain the x-coordinate and y-coordinate in the plane rectangular coordinate system.

[0132] Step 3: Based on the Gauss-Kruger projection coordinate transformation relationship, the pixel points are transformed into Cartesian coordinates, and the transformed coordinates of the pixel points are marked as transformed coordinates. Based on the comparison relationship between the transformed coordinates and the projected coordinates, if the transformed coordinates and the projected coordinates are inconsistent on the same Cartesian coordinate system, the image is resampled to generate a sampled signal.

[0133] Step 4: Based on the sampled signal, the image is resampled using bilinear interpolation to obtain a sampled image. The difference between the pixel value of the sampled image and the corresponding pixel value in the original image is calculated to obtain the sampled pixel difference. The sampled pixel difference is compared with the sampled pixel difference threshold. If the sampled pixel difference is greater than the sampled pixel difference threshold, the pixel is marked as a deteriorated pixel. Based on the deteriorated pixels, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. Based on the proportion of deterioration quantity and the proportion of deterioration degree, the deterioration performance value is obtained.

[0134] Step 5: Based on the adjustment signal, establish the fitting curves of transformed coordinates and projected coordinates, obtain error data, and adjust the deteriorated coordinates according to the error data;

[0135] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0136] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0137] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0138] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0139] In another embodiment of the present invention, a medium is also provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described calculation method.

[0140] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An image blur correction method, characterized in that: include: Obtain a blurred image, select multiple pixels evenly within the image range to ensure coverage of all areas, and establish a Gauss-Kruger projection model; Based on the Gauss-Kruger projection model, auxiliary parameters are obtained using the WGS84 standard ellipsoid model as a reference. The coordinate transformation formula of the Gauss-Kruger projection is then calculated based on the auxiliary parameters to obtain the abscissa and ordinate in the Cartesian coordinate system. Based on the Gauss-Kruger projection coordinate transformation relationship, the pixel points are transformed into Cartesian coordinates and the transformed coordinates of the pixel points are marked as transformed coordinates. Based on the comparison relationship between the transformed coordinates and the projected coordinates, if the transformed coordinates and the projected coordinates are inconsistent in the same Cartesian coordinate system, the image is resampled to generate a sampled signal. Based on the sampled signal, the image is resampled using bilinear interpolation to obtain a sampled image. The difference between the pixel value of the sampled image and the corresponding pixel value in the original image is calculated to obtain the sampled pixel difference. The sampled pixel difference is compared with a sampled pixel difference threshold. If the sampled pixel difference is greater than the sampled pixel difference threshold, the pixel is marked as a deteriorated pixel. Based on the deteriorated pixels, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. Based on the proportion of deterioration quantity and the proportion of deterioration degree, the deterioration performance value is obtained. Based on the adjustment signal, a fitting curve for the transformed coordinates and projected coordinates is established, error data is obtained, and the deteriorated coordinates are adjusted according to the error data.

2. The image blur correction method according to claim 1, characterized in that: The percentage of deterioration is labeled SL, and the percentage of deterioration severity is labeled CD. Through formula The deterioration performance value BX is calculated, where s1 and s2 are preset proportional coefficients.

3. The image blur correction method according to claim 2, characterized in that: The method for obtaining the percentage of deteriorated numbers is as follows: Compare all optimized pixel values ​​with the original pixel values; If the optimized pixel value is greater than or equal to the original pixel value, then this pixel is marked as an improved pixel. If the optimized pixel value is less than the original pixel value, then this pixel is marked as a deteriorated pixel. The number of degraded pixels is counted, and the ratio of the number of degraded pixels to the total number of pixels is calculated to obtain the percentage of degraded pixels.

4. The image blur correction method according to claim 2, characterized in that: The method for obtaining the percentage of deterioration is as follows: The mean value of degraded pixels is calculated by summing all the degraded pixel values ​​and taking the average. The difference between the mean value of the degraded pixels and the value of the degraded pixels is calculated, and the absolute value is taken to obtain the degraded pixel deviation value. The difference between the deteriorated pixel deviation value and the deteriorated pixel mean value is calculated and the absolute value is taken to obtain the deteriorated pixel mean deviation value. The average value of the original pixels is obtained by summing all the original pixel values ​​and taking the mean value. The ratio of the average deviation of the deteriorated pixels to the average value of the original pixels is calculated to obtain the percentage of deterioration.

5. The image blur correction method according to claim 1, characterized in that: The percentage of overlapping curves is SV, and the percentage of remaining area is marked as IP. Through formula The error characterization value is calculated, where a1 and a2 are both preset proportional coefficients.

6. The image blur correction method according to claim 5, characterized in that: The method for obtaining the percentage of curves in the overlapping phase is as follows: The straight line containing the abscissa of the coordinate system is divided into several sub-stages, and the number of overlapping stage curves is measured. The overlapping stage curves represent the stage curves that overlap between the transformed coordinate fitting curve and the projected coordinate fitting curve. The ratio of the number of overlapping stage curves to the total number of stage curves is calculated to obtain the percentage of overlapping stage curves.

7. The image blur correction method according to claim 5, characterized in that: The method for obtaining the remaining area percentage is as follows: Extend the endpoints of the two curves toward the X-axis to obtain the figure enclosed by the curves and the X-axis; The areas of the figures enclosed by the two curves and the X-axis, as well as the areas of the overlapping parts of the two curves, are calculated using mathematical methods. The resulting areas are the overlapping area of ​​the curves, the area of ​​the transformed coordinate curve, and the area of ​​the projected coordinate curve. The difference between the area of ​​the transformed coordinate curve and the area of ​​the curve that coincides with the curve is calculated, and the absolute value is taken to obtain the remaining area after transformation. The difference between the area of ​​the projected coordinate curve and the area of ​​the curve that coincides with the curve is calculated, and the absolute value is taken to obtain the remaining projected area. The total remaining area is obtained by summing the transformed remaining area and the projected remaining area. The ratio of the total remaining area to the area of ​​the projected coordinate curve is calculated to obtain the percentage of the remaining area.

8. An image blur correction device, characterized in that, include: Coordinate acquisition module: Based on the fuzzy image, the image is projected onto a standard map, and pixels that are evenly distributed and cover the entire image are selected to establish a Gauss-Kruger projection model. Model building module: Based on the Gauss-Kruger projection model, auxiliary parameters are obtained using the WGS84 standard ellipsoid model as a reference. Based on the auxiliary parameters, the coordinate transformation formula of the Gauss-Kruger projection is calculated to obtain the abscissa and ordinate in the plane rectangular coordinate system. Signal acquisition module: Based on the Gauss-Kruger projection coordinate transformation relationship, the pixel points are transformed into Cartesian coordinates and the transformed coordinates of the pixel points are marked as transformed coordinates. Based on the comparison relationship between the transformed coordinates and the projected coordinates, if the transformed coordinates and the projected coordinates are inconsistent on the same Cartesian coordinate system, the image is resampled to generate a sampled signal. Sampling signal analysis module: Based on the sampling signal, the image is resampled using bilinear interpolation to obtain a sampled image. The difference between the pixel value of the sampled image and the corresponding pixel value in the original image is calculated to obtain the sampling pixel difference. The sampling pixel difference is compared with the sampling pixel difference threshold. If the sampling pixel difference is greater than the sampling pixel difference threshold, the pixel is marked as a deteriorated pixel. Based on the deteriorated pixels, the proportion of deterioration quantity and the proportion of deterioration degree are obtained. Based on the proportion of deterioration quantity and the proportion of deterioration degree, the deterioration performance value is obtained. Image adjustment module: Based on the adjustment signal, it establishes a fitting curve for the transformed coordinates and projected coordinates, obtains error data, and adjusts the deteriorated coordinates according to the error data.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the method described in any one of claims 1-7.

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