Image enhancement method and device

Abstract

The present application discloses an image enhancement method and device. The method includes: obtaining a first straight line segment existing in an original image; obtaining a second straight line segment from the first straight line segment according to the length of the first straight line segment, and determining a first image block from the original image according to the position of the second straight line segment in the original image; rotating and / or translating the first image block to obtain a second image block, and obtaining reference coordinates of pixels in the first image block corresponding to the pixel coordinate system according to the pixel coordinate system of the second image block; obtaining sub-pixel grayscale values ​​of pixels in the second image block according to the reference coordinates of each pixel in the first image block and the grayscale value of each pixel in the first image block; obtaining a ground marker according to the grayscale mean and texture uniformity value of the second image block, and calculating the MTF function corresponding to the ground marker; constructing a two-dimensional MTF matrix according to the MTF function, and using the two-dimensional MTF matrix to enhance the original image.

Description

Technical Field

[0001] The present application relates to a multi-spectral original image edge enhancement technology, and in particular to an image enhancement method and device. Background Art

[0002] In remote sensing image processing, image enhancement techniques based on modulation transfer function (MTF) are widely used in the correction of satellite raw images. Since the performance of satellite sensors degrades during on-orbit operation, the MTF of the sensors measured in the laboratory cannot meet the requirements of long time series and high precision, and it is necessary to measure it on-orbit. Usually, on-orbit measurement of MTF requires obtaining ground markers with fixed angle features (such as 0°, 45° or 90°), but in remote sensing images, it is often difficult to find markers with precise fixed angle features. Summary of the invention

[0003] The present application provides an image enhancement method and device.

[0004] In a first aspect, an image enhancement method is provided. The method comprises:

[0005] Obtain the first straight line segment existing in the original image;

[0006] Obtaining a second straight line segment from the first straight line segment according to the length of the first straight line segment, and determining a first image block from the original image according to the position of the second straight line segment in the original image;

[0007] Rotate and / or translate the first image block to obtain a second image block, and obtain reference coordinates of each pixel in the first image block in the pixel coordinate system according to the pixel coordinate system of the second image block;

[0008] Obtaining a grayscale value of each pixel in the second image block according to the reference coordinates of each pixel in the first image block and the grayscale value of each pixel in the first image block; wherein the grayscale value of each pixel in the second image block is a sub-pixel grayscale value;

[0009] Obtaining a ground marker according to the grayscale mean and texture uniformity value of the second image block, and calculating the MTF function corresponding to the ground marker;

[0010] A two-dimensional MTF matrix is ​​obtained according to the MTF function, and the original image is enhanced using the two-dimensional MTF matrix.

[0011] In a possible implementation, obtaining a ground marker based on the grayscale mean and texture uniformity value of the second image block includes: obtaining a ground marker that meets a preset regional grayscale difference threshold range and a preset texture uniformity threshold range from the second image block; wherein the texture uniformity value is represented by the IDM value of the second image block.

[0012] In a possible implementation manner, calculating the MTF function corresponding to the ground marker includes:

[0013] For any i-th row of pixel sequences in the ground marker, calculate the position of the second edge point in the i-th row of pixel sequences according to the grayscale value of each pixel in the i-th row of pixel sequences;

[0014] Obtaining a second grayscale distribution function of the i-th row of pixel sequence according to the position of the second edge point in the i-th row of pixel sequence;

[0015] Accumulating and averaging the second grayscale distribution function of each row of pixel sequences in the ground marker to obtain a third grayscale distribution function of the ground marker;

[0016] According to the third grayscale distribution function of the ground marker, the MTF function of the ground marker is calculated.

[0017] In a possible implementation, for any i-th row of pixel sequences in the ground marker, calculating the position of the second edge point in the i-th row of pixel sequences according to the grayscale value of each pixel in the i-th row of pixel sequences includes:

[0018] For any i-th row of pixel sequences in the ground marker, obtaining a first edge point in the i-th row of pixel sequences according to the grayscale value of each pixel in the i-th row of pixel sequences;

[0019] Calculating a grayscale threshold of the pixel sequence in the i-th row according to the grayscale value of each pixel in the pixel sequence in the i-th row;

[0020] Fitting the grayscale values ​​of a number of pixels on both sides of the first edge point to obtain a first grayscale distribution function of the pixel sequence in the i-th row;

[0021] Substitute the grayscale threshold into the first grayscale distribution function to obtain the sub-pixel position of the second edge point of the i-th row of pixel sequence.

[0022] In a possible implementation, calculating the grayscale threshold of the i-th row of pixel sequence based on the grayscale value of each pixel in the i-th row of pixel sequence includes: calculating the grayscale threshold of the i-th row of pixel sequence by Otsu's method based on the grayscale value of each pixel in the i-th row of pixel sequence.

[0023] In a possible implementation manner, the step of accumulating and averaging the second grayscale distribution functions of the pixel sequences in each row of the ground marker to obtain a third grayscale distribution function of the ground marker comprises:

[0024] Detecting the grayscale threshold of any i-th row pixel sequence of the ground marker to obtain a second pixel sequence of the ground marker;

[0025] The second grayscale distribution function of the second pixel sequence in each row of the ground marker is accumulated and averaged to obtain a third grayscale distribution function of the ground marker.

[0026] In a possible implementation, the second grayscale distribution function of each row of pixel sequences in the ground marker is accumulated and averaged to obtain a third grayscale distribution function of the ground marker; including:

[0027] Detecting the second edge points of each row of pixel sequences of the ground marker according to the random sampling consistency function to obtain a third pixel sequence of the ground marker;

[0028] A third grayscale distribution function of the ground marker is obtained according to the second grayscale distribution function of the third pixel sequence in each row of the ground marker.

[0029] In a possible implementation, a two-dimensional MTF matrix is ​​calculated according to the MTF function corresponding to the ground marker, including: according to the MTF function corresponding to the ground marker, a two-dimensional Gaussian function model is used for simulation to construct a two-dimensional MTF matrix.

[0030] In a second aspect, an image enhancement device is provided, comprising:

[0031] A line segment acquisition unit, used for acquiring a first straight line segment existing in the original image;

[0032] an image determining unit, which obtains a second straight line segment from the first straight line segment according to a length of the first straight line segment, and determines a first image block from the original image according to a position of the second straight line segment in the original image;

[0033] a coordinate calculation unit, configured to rotate and / or translate the first image block to obtain a second image block, and obtain reference coordinates of each pixel in the first image block in the pixel coordinate system according to the pixel coordinate system of the second image block;

[0034] a grayscale calculation unit, configured to obtain a grayscale value of each pixel in the second image block according to a reference coordinate of each pixel in the first image block and a grayscale value of each pixel in the first image block; wherein the grayscale value of each pixel in the second image block is a sub-pixel grayscale value;

[0035] A function acquisition unit, used to obtain a ground marker according to the grayscale mean value and the texture uniformity value of the second image block, and calculate the MTF function corresponding to the ground marker;

[0036] The image enhancement unit is used to obtain a two-dimensional MTF matrix according to the MTF function, and use the two-dimensional MTF matrix to enhance the original image.

[0037] According to a third aspect, a computing device is provided, comprising a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method described in any one of the first aspects is implemented.

[0038] In the technical solution provided in the present application, a second straight line segment that meets the length range is obtained by screening according to the length of the first straight line segment at any angle existing in the original image, and the image block corresponding to the second straight line segment is confirmed in the original image; the image block corresponding to the second straight line segment is translated and / or rotated; and based on the grayscale value and texture uniformity of the image block, a ground marker that meets the characteristics for calculating the MTF function is obtained from the image block, thereby solving the problem that the MTF function can only be calculated based on ground markers with fixed angle characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic diagram of a process of an image enhancement method provided in an embodiment of the present application;

[0041] Figure 2 This is one of the schematic diagrams of a first image block exemplarily provided in an embodiment of the present application;

[0042] Figure 3 The second schematic diagram of a first image block exemplarily provided in an embodiment of the present application;

[0043] Figure 4 A sample diagram of ground markers provided as an example in the embodiments of the present application;

[0044] Figure 5 for Figure 1 A flowchart of a specific implementation of step S111;

[0045] Figure 6 for Figure 5 A flowchart of a specific implementation of step S1111;

[0046] Figure 7 A curve diagram of a third grayscale distribution function of a ground marker exemplarily provided in an embodiment of the present application;

[0047] Figure 8 This is an MTF function curve diagram of a ground marker provided as an example in an embodiment of the present application;

[0048] Fig. 9 This is one of the comparison diagrams of the original image enhancement effect provided as an example in the embodiments of the present application;

[0049] Fig.10 This is the second comparison diagram of the original image enhancement effect provided as an example in the embodiment of the present application;

[0050] Fig.11 This is the third comparison diagram of the original image enhancement effect provided as an example in the embodiments of the present application;

[0051] Fig.12 This is a fourth comparison diagram of the original image enhancement effect provided as an example in the embodiments of the present application;

[0052] Fig.13 Schematic diagram of the structure of an image enhancement device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The solution provided in this specification is described below in conjunction with the accompanying drawings.

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be described below in conjunction with the accompanying drawings. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0055] In the description of the embodiments of the present application, words such as "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a concrete way.

[0056] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the term "plurality" means two or more.

[0057] In the description of the embodiments of the present application, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or are the positions or positional relationships in which the product of the application is usually placed when in use. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0058] In the description of the embodiments of the present application, the terms "horizontal", "vertical", "overhanging", "vertical" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0059] In addition, in the description of the embodiments of the present application, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0060] A remote sensing platform is a tool used to carry sensors in the remote sensing process. It is like a tripod for placing a camera when taking photos on the ground. It is a device for placing sensors in the air or space. At present, the main remote sensing platforms include high-altitude balloons, airplanes, rockets, artificial satellites, manned spacecraft, etc. The remote sensing platform uses the sensors it carries to sense the radiation or reflected electromagnetic waves of the ground environment at a long distance. Among them, in addition to visible light cameras, infrared cameras, and ultraviolet cameras, there are also infrared scanners, multi-spectral scanners, microwave radiation and scatterometers, side-looking radars, thematic imagers, imaging spectrometers, etc. The sensor receives digital and image information, and usually uses three recording methods: film, image, and digital tape. Its information is provided to users for analysis and interpretation through optical processing such as correction, transformation, decomposition, and combination, or image digital processing processes, or with the support of geographic information systems and expert systems, it is made into thematic maps or statistical charts to provide information services for resource exploration, environmental monitoring, land surveying and mapping, and military reconnaissance.

[0061] In recent years, satellite maps have gradually been integrated into the public's online life. Both operators and the general public have a new understanding and usage habits of real-life satellite images. At present, there are map service providers that have launched satellite maps. Through the images on the map, users can clearly see buildings, streets, and garden landscapes. As the application technology becomes more and more mature, the satellite map industry chain can cover many links, such as meteorology, marine fishing, national defense, etc., and provide more abundant services.

[0062] After obtaining the original satellite image data, satellite image users still need to go through many production links such as "selecting original images, coordinate correction, image color adjustment, mosaicking, quality inspection and drawing", and the entry threshold is very high. The original image is the unprocessed instrument data and the payload data with full resolution. It is physically modeled through radiation correction, geometric correction, orthographic projection and other methods to generate more advanced products. Due to the influence of satellite in-orbit attitude adjustment, equipment aging, radiation performance degradation, etc., the original image has the problem of deterioration of quality and blurred edge clarity.

[0063] As one of the remote sensing platforms, the SDGSAT-1 satellite was developed by the Chinese Academy of Sciences' "Earth Big Data Science Project" pilot project. It is the world's first scientific satellite dedicated to the United Nations 2030 Agenda for Sustainable Development. It is equipped with three sensors: multi-spectral, low-light and thermal infrared imagers. The multispectral sensor uses a push-broom imaging system to obtain multispectral images with a resolution of 10m. A complete original image of the SDGSAT-1 satellite's high-resolution multispectral sensor (Multispectral Imager for Inshore, MII) is taken by two cameras.

[0064] In the field of remote sensing image processing, Modulation Transfer Function (MTF) is a typical indicator for evaluating the performance of optical imaging systems. Image enhancement technology based on Modulation Transfer Function Compensation (MTFC) has been applied to the radiation correction of satellite raw images. The MTF of the sensor can be obtained by ground laboratory measurement before the satellite is launched, or by in-orbit monitoring after the launch. Laboratory measurement uses professional measuring instruments to calculate MTF, which has the advantages of high accuracy and good reliability. However, due to the attenuation of the sensor's on-orbit performance, the MTF measured in the laboratory cannot guarantee the edge clarity requirements of long-term high-precision enhanced images. In-orbit monitoring uses artificially laid ground targets or selected ground markers to calculate MTF on the image. Ground markers are mainly large-scale scene objects such as airports, bridges, and coastlines; however, these objects often require manual visual interpretation, which is time-consuming and labor-intensive and relies on cloudless and clear atmospheric imaging conditions, making it difficult to efficiently track the MTF indicators of the sensor. In addition, some current automated MTF calculation methods still rely on finding ground markers with fixed angle features (90°, 0°, and 45°). However, it is difficult to find precise ground markers with vertical 90°, horizontal 0°, and tilted 45° in remote sensing images. Solving these problems is the key to improving the MTF enhancement efficiency of domestic high-resolution multispectral images.

[0065] In response to the above problems, at least one image enhancement method and device is provided in the embodiments of the present application.

[0066] like Figure 1 As shown, the method may include but is not limited to the following steps S101 to S113.

[0067] S101, obtaining a first straight line segment existing in the original image.

[0068] The original image is obtained from the satellite, and multiple sliding windows are set on the original image. The sliding windows slide with a fixed step length. During the sliding process, the first straight line segment in the original image is extracted from the sliding window. The first straight line segment is a line segment that actually exists in the original image and has an arbitrary angle.

[0069] For example, the original files taken by the MII sensor of the SDGSAT-1 satellite at different dates and angles are obtained, and multiple original images are obtained from them to extract ground landmarks. Then, the high-frequency noise in the original image is suppressed by data processing technology, wherein the data processing technology includes discrete cosine transform (DCT) technology. Then, in the original image, multiple sliding windows are set, each sliding window slides with a fixed step length, and multiple sliding windows can cover the entire original image block; in the sliding process of multiple sliding windows, the first straight line segment is extracted from the original image. For example, in the original image, a sliding window of size 1000*1000 is set, and the sliding step length is 500 pixels.

[0070] S103, obtaining a second straight line segment from the first straight line segment according to the length of the first straight line segment; and determining a first image block from the original image according to the position of the second straight line segment in the original image.

[0071] According to the predetermined length range, a second straight line segment that meets the predetermined first length range is obtained in the first straight line segment.

[0072] In the original image, according to the position of the second straight line segment in the original image, a rectangular image block with the second straight line segment as the center line is obtained, that is, the first image block; wherein a first side of the first image block is parallel to the second straight line segment and is equal in length to the second straight line segment, and a second side of the first image block that is perpendicular to the first side is a predetermined length.

[0073] For example, the first length range is 40-70 pixels, and a second straight line segment A that meets the first length range is obtained from the first straight line segment, and the length of the second straight line segment A is 50 pixels. The predetermined length of the second side of the first image block A1 corresponding to the second straight line segment A is 200 pixels. In the original image, according to the position of the second straight line segment A in the original image block and the predetermined length of the first image block A1 of 50 pixels, the first image block A1 is obtained. Among them, the position of the second straight line segment A is the center line of the first image block A1; in the first image block A1, the length of the first side parallel to the second straight line segment A is 50 pixels, and the length of the second side perpendicular to the second straight line segment A is 200 pixels; the distance between the second straight line segment A and the two first sides parallel to the second straight line segment A in the first image block A1 and the second straight line segment A is 100 pixels respectively.

[0074] S105, rotating and / or translating the first image block to obtain a second image block, and obtaining reference coordinates of each pixel in the first image block in the pixel coordinate system of the second image block according to the pixel coordinate system of the second image block.

[0075] In the original image, the position of the second straight line segment has a first angle with the first side of the original image, and the first angle is 0-90°; the first image block corresponding to the second straight line segment whose first angle is not 0° is rotated along the first direction by the angle of the first angle, and the second image block is obtained after translation. The first direction is the direction of the least angle required to make the second straight line segment parallel to the first side of the original image. When the first angle is 0°, there is no need to rotate the first image block.

[0076] In the second image block, a coordinate system is established with the first vertex as the origin, the second side perpendicular to the second straight line segment as the x-axis, and the first side parallel to the second straight line segment as the y-axis to obtain the coordinates of each pixel in the second image block. According to the displacement of the first image block corresponding to the second image block, the first angle, and the coordinates of each pixel in the second image block, the reference coordinates of each pixel in the first image block corresponding to the pixel coordinate system of the second image block are obtained; wherein, the coordinates of each pixel in the first image block corresponding to the pixel coordinate system of the second image block are expressed as the coordinates of each pixel in the first image block in the following. wherein, the displacement of the first image block can be calculated according to the coordinates of the vertex of the second straight line segment, the first angle, and the predetermined second length.

[0077] Then, the grayscale value of each pixel in the second image block is obtained according to the coordinates of each pixel in the first image block and the grayscale value of each pixel in the first image block, wherein the grayscale value at each pixel in the first image block is obtained from the grayscale value of the original image.

[0078] The coordinates of each pixel of the first image block can be obtained as follows:

[0079] For example, the coordinates of the pixels in the first image block corresponding to each pixel in the second image block can be calculated by Formula 1:

[0080]

[0081] Wherein, y and x are the coordinates of the pixel in the first image block corresponding to the pixel in the second image block, and y′ and x′ are the coordinates of the pixel in the second image block; θ is the first angle of the second straight line segment, and when the first direction is clockwise, θ takes a negative value, otherwise, θ takes a positive value; Δx and Δy are the translation amounts of the first image block on the x-axis and y-axis, respectively. The translation amount of the first image block can be calculated based on the coordinates of the vertices of the second straight line segment, the first angle, and the lengths of the first and second sides of the second image block.

[0082] Exemplarily, the following two examples are used to illustrate the calculation method of the translation amounts Δx and Δy of the first image block.

[0083] For example Figure 2As shown, the first angle of the second straight line segment is θ, the first image block rotates clockwise by θ, and is translated by Δx and Δy pixels on the x-axis and y-axis respectively. Figure 2 Point a is the origin of the second image block after rotation. When the first direction is clockwise, the translation amounts Δx and Δy of the first image block can be calculated according to the following formulas 2 and 3:

[0084]

[0085]

[0086] Among them, Δx and Δy are the translation amounts of the first image block on the x-axis and y-axis respectively; x1 is the distance from the first vertex of the second straight line segment in the first image block to the y-axis; y1 is the distance from the first vertex of the second straight line segment in the first image block to the x-axis; Width is the length of the second side of the second image block; θ is the first angle of the second straight line segment.

[0087] For example Figure 3 As shown, the first angle θ of the second straight line segment rotates the first image block counterclockwise by θ and translates Δx and Δy pixels on the x-axis and y-axis respectively. When the first direction is counterclockwise, the translation amounts Δx and Δy of the first image block can be calculated according to the following formulas 4 and 5:

[0088]

[0089]

[0090] Among them, Δx and Δy are the translation amounts of the first image block on the x-axis and y-axis respectively; x2 is the distance from the second vertex of the second straight line segment in the first image block to the y-axis; y2 is the distance from the second vertex of the second straight line segment in the first image block to the x-axis; Width is the length of the second side of the second image block; θ is the first angle of the second straight line segment.

[0091] S107, obtaining the grayscale value of each pixel in the second image block according to the coordinates of each pixel in the first image block and the grayscale value of each pixel in the first image block; wherein the grayscale value of each pixel in the second image block is at a sub-pixel level.

[0092] In the original image, the grayscale value of each pixel in the first image block in the original image is obtained according to the coordinates of each pixel in the first image block. Then, according to the grayscale value of each pixel in the first image block, the sub-pixel grayscale value of each pixel in the second image block is obtained by sub-pixel interpolation.

[0093] At the same time, since the grayscale value at each pixel in the second image block is obtained by projecting the grayscale value at the pixel in the first image block, for the convenience of calculation, the coordinates of each pixel in the first image block can be used to interpolate and calculate the grayscale value of the pixel in the second image block; for example, the grayscale value of the pixel in the second image block can be obtained by the following formulas 6 and 7:

[0094]

[0095]

[0096] Wherein, DN(x',y') is the gray value of the pixel (x',y') in the second image block; x',y' represents the coordinates of the pixel in the second image block; Respectively represent the grayscale values ​​of the four pixels closest to the interpolation point in the first image block; They respectively represent the coordinates of the pixels closest to the interpolation point in the first image block; r and s respectively represent the relative offsets of the pixels in the first image block, where the values ​​of r and s are [0,1]; int(x) and int(y) are the rounding functions of the coordinates x and y at the sub-pixel (x,y) in the first image block, respectively.

[0097] S109: Obtain ground landmarks according to the grayscale mean and texture uniformity value of the second image block.

[0098] From the second image block, a ground marker that meets a preset regional grayscale difference threshold range and a preset texture uniformity threshold range is obtained.

[0099] According to the gray value of each pixel in the second image block, the gray mean of each partial image on both sides of the second straight line segment in the second image block is calculated, and the texture uniformity value of the second image block is calculated. Subsequently, from the second image block, a second image block that meets the preset regional gray value difference threshold range and the preset texture uniformity threshold range is obtained and used as a ground marker.

[0100] Among them, each partial image is an image block on one side of the second straight line segment in the second image block; the second straight line segment is one side of the partial image; the length of the other side connected to the second straight line segment in the partial image is half the length of the second side of the second image block; a single second image block has two partial images of equal size. The regional grayscale difference is the difference between the grayscale mean values ​​of the first partial image and the second partial image in the second image block. The texture uniformity value is represented by the inverse difference moment (IDM), and the IDM value of each partial image on both sides of the second straight line segment in the second image block can be calculated based on the gray-level co-occurrence matrix (GLCM) of the second image block and the GLCM value of the pixel gray value pair in the GLCM.

[0101] The GLCM values ​​of the partial images on both sides of the second image block are obtained from the grayscale values ​​of each pixel.

[0102] For example, in the second image block A2, the length of the second straight line segment A is 50 pixels, and the second side of the second image block A2 is 200 pixels. From the second image block A2, the partial image B1 on the first side of the second straight line segment A includes 100*50 pixels in total, where the second straight line segment A is the first side of the partial image B1; the grayscale mean of the 100*50 pixels of the partial image B1 is 50.

[0103] In the second image block A2, the partial image B2 on the second side of the second straight line segment A includes 100*50 pixels in total, wherein the second straight line segment A is another third side of the partial image B2 parallel to the first side; the grayscale average value of the 100*50 pixels of the partial image B2 is 10.

[0104] The grayscale difference of the second image block is the difference between the grayscale mean of 50 of partial image B1 and the grayscale mean of 10 of partial image B2, that is, the grayscale difference of the second image block is 40, which is greater than the predetermined grayscale difference threshold of 30 and meets the grayscale threshold range of the ground marker.

[0105] Exemplarily, the IDM value of a partial image may be calculated using the following formula 8.

[0106]

[0107] Among them, g (i,j) is the GLCM value of the pixel gray value pair (i, j) in the partial image of the second image block, i and j are the grayscales of the two grayscale values ​​in the pixel grayscale value pair of the partial image of the second image block, and L is the maximum grayscale level of the partial image in the second image block.

[0108] For example, in the second image block A2, the length of the second straight line segment A is 50 pixels, and the second side of the second image block A2 is 200 pixels. The partial image B1 in the second image block A2 includes 100*50 pixels in total, and the IDM value of the 100*50 pixels of the partial image B1 is 0.989.

[0109] In the second image block A2, on the second side of the second straight line segment A, the partial image B2 includes 100*50 pixels in total; the IDM value of the 100*50 pixels of the partial image B2 is 0.991.

[0110] In the second image block A2, the IDM value of the partial image B1 of 0.989 and the IDM value of the partial image B2 of 0.991 are both greater than the preset IDM value of 0.98, so the second image block A2 meets the texture uniformity threshold range of the ground marker.

[0111] Figure 4 The ground markers C, D, E, and F extracted according to step S204 are shown. The ground markers C, D, E, and F are extracted from the image imaged by the MII sensor camera of the SDGSAT-1 satellite on December 21, 2022, wherein the size of the image is 30,000 pixels per row and 15,000 pixels per column. Among them, the first angle of the second straight line segment corresponding to the ground marker C is 53.55°, the first angle of the second straight line segment corresponding to the ground marker D is 48.09°, the first angle of the second straight line segment corresponding to the ground marker E is 58.71°, and the first angle of the second straight line segment corresponding to the ground marker F is 30.77°.

[0112] S111, calculating the MTF function corresponding to the ground marker according to the ground marker.

[0113] More specifically, according to Figure 5 The MTF function is calculated by performing steps S1111 to S1117 as shown.

[0114] S1111, for any i-th row of pixel sequences in the ground marker, calculate the sub-pixel position of the edge point in the i-th row of pixel sequences according to the grayscale value of each pixel in the i-th row of pixel sequences.

[0115] In a possible implementation, S1111 may include: Figure 6 The steps S201-S207 shown are used to calculate the sub-pixel position of the edge point in the i-th row of pixel sequence.

[0116] S201, for any i-th row of pixel sequences in the ground marker, obtain the first edge point in the i-th row of pixel sequences according to the grayscale value of each pixel in the i-th row of pixel sequences.

[0117] For any i-th row of pixel sequences in the ground marker, the grayscale values ​​of the pixels in the i-th row of pixel sequences are differentially calculated according to the grayscale values ​​of each pixel in the i-th row of pixel sequences, and the pixel with the maximum grayscale value differential in the i-th row of pixel sequences is selected as the first edge point in the i-th row of pixel sequences. Specifically, the pixel with the largest grayscale difference between the adjacent pixels on the first side and the adjacent pixels on the second side of the pixel in the i-th row of pixel sequences is selected.

[0118] S203, calculating the grayscale threshold of the pixel sequence in the i-th row according to the grayscale value of each pixel in the pixel sequence in the i-th row.

[0119] The grayscale threshold of the pixel sequence in the i-th row is the grayscale value that distinguishes each pixel in the pixel sequence in the i-th row as foreground or background. The grayscale threshold of the pixel sequence in the i-th row can be calculated by using some threshold selection algorithms, such as the maximum inter-class variance method, the entropy-based method, the Otsu method, etc.

[0120] For example, the Otsu method may be used to calculate the grayscale threshold of the pixel sequence in the i-th row.

[0121] First, according to the grayscale value of each pixel in the i-th row of pixel sequence, the occurrence frequency of each grayscale value is calculated; secondly, according to the grayscale value and corresponding frequency of each pixel in the i-th row of pixel sequence, the grayscale average value of all pixels in the i-th row of pixel sequence and the total number of pixels in the i-th row of pixel sequence are calculated;

[0122] Then, the grayscale values ​​of all pixels in the i-th row of pixel sequence are traversed, and the intra-class variance of the foreground and background in the i-th row of pixel sequence is calculated at each grayscale value; and the grayscale value that minimizes the intra-class variance is found, which is the grayscale threshold of the i-th row of pixel sequence.

[0123] S205 , fitting the grayscale values ​​of a number of pixels on both sides of the first edge point position in the i-th row pixel sequence to obtain a first grayscale distribution function of the i-th row pixel sequence.

[0124] For the i-th row of pixel sequence, at the first edge point in the i-th row of pixel sequence, the grayscale values ​​of a certain number of pixels on the first side and the second side are fitted to obtain the first grayscale distribution function of the i-th row of pixel sequence, wherein the abscissa of the first grayscale distribution function of the i-th row of pixel sequence is the grayscale value, and the ordinate is the relative position of each pixel in a certain number of pixels.

[0125] For example, for the first edge point position in the i-th row of pixel sequence, 4 pixels on the first side and 4 pixels on the second side are obtained, for a total of 8 pixels; and the 8 pixels are numbered in the order from the first side to the second side, and the numbers are integers from 0 to 7. Subsequently, the grayscale values ​​of the 8 pixels are fitted. Among them, the x-axis of the first grayscale distribution function of the i-th row of pixel sequence is the grayscale value of the pixel, and the y-axis is the position of the pixel, and the y-axis value can be the number of each of the 8 pixels.

[0126] Exemplarily, a Logistic function as shown in Formula 9 may be used for fitting.

[0127]

[0128] y (i,j) =j(10)

[0129] Among them, y (i,j) is the position of the j-th pixel (i, j) in the i-th row of the pixel sequence, and the value of j ranges from 0 to 7; x (i,j) is the gray value of the jth pixel (i, j) in the i-th row pixel sequence; a i 、b i 、c i ,d i These are all parameters to be determined in the fitting function.

[0130] Then, the Levenberg-Marquardt algorithm (LM) is used to solve the parameters of the first grayscale distribution function corresponding to the pixel sequence in the i-th row, as shown in the following formulas 11 and 12. The damping term is introduced in the LM algorithm, and the substitution parameter a of formula 9 is solved by converting the unconstrained least squares problem into a constrained least squares problem. i 、b i 、c i ,d i , thereby obtaining the first grayscale distribution function of the pixel sequence in the i-th row.

[0131]

[0132]

[0133] (J T J+μI)h=-J T f(13)

[0134] Where f is the loss function; y (i,j) , are the positions of the jth pixel point (i, j) in the i-th row of pixel sequence before and after the calculation respectively; G(h) is the first grayscale distribution function, that is, the objective function to be solved; fT f represents the sum of the squares of the grayscale values ​​of each pixel in the sequence of the i-th row of pixels; h is the parameter of the first grayscale distribution function; J represents the Jacobian matrix, which is the partial derivative of the objective function G(h) with respect to the parameter h; h T J T f is the inner product of the objective function gradient and the parameter; h T J T Jh represents the quadratic form of the objective function gradient and parameters; μ is the weight regularization parameter that controls the regularization term; I represents the identity matrix; (J T J+μI) is part of the regularization term; -J T f is the relationship between the observed data and the gradient of the objective function.

[0135] S207, substituting the grayscale threshold of the pixel sequence in the i-th row into the first grayscale distribution function to obtain the position of the second edge point of the pixel sequence in the i-th row.

[0136] Substitute the grayscale threshold of the pixel sequence in the i-th row into the x of the first distribution function (i,j) In the above example, we obtain (i,j) That is, the position of the second edge point of the pixel sequence in the i-th row. The position of the second edge point of the pixel sequence in the i-th row is a sub-pixel position.

[0137] In addition, in step S1111, the position of the second edge point of the pixel sequence in the i-th row may be obtained by other methods other than the aforementioned steps S201-S207. For example, the second edge point in the image is identified by edge detection algorithms such as the Canny algorithm and the Sobel algorithm, and then a sub-pixel interpolation algorithm such as bilinear interpolation or bicubic interpolation is used to obtain the sub-pixel position of the second edge point according to the pixel values ​​around the second edge point.

[0138] Step 1113: Obtain a second grayscale distribution function of the i-th row of pixel sequence according to the position of the second edge point in the i-th row of pixel sequence.

[0139] For the i-th row of pixel sequence, at the position of the second edge point of the i-th row of pixel sequence, a certain number of pixels on the first side and the second side are interpolated at a certain distance to obtain the second grayscale distribution function of the i-th row of pixel sequence.

[0140] For example, for the i-th row of pixel sequence, at the position of the second edge point of the i-th row of pixel sequence, 7 pixels on the first side and 7 pixels on the second side are selected, interpolation is performed at intervals of 0.05 pixels, and histogram statistics are used to obtain the second grayscale distribution function of the i-th row of pixel sequence.

[0141] S1115, obtaining a third grayscale distribution function for the ground marker according to the second grayscale distribution function of each row of pixel sequences in the ground marker.

[0142] The second grayscale distribution function of each row of pixel sequences in the ground marker is accumulated and averaged, and the noise of the averaged function is eliminated to obtain the third grayscale distribution function of the ground marker.

[0143] For example, the second grayscale distribution function of each row of pixel sequences of the ground marker is accumulated and averaged, and the result is smoothed and filtered to eliminate noise, so as to obtain the following: Figure 7 The third grayscale distribution function of the ground marker is shown. The third grayscale distribution function of the ground marker is an edge extension smoothing function.

[0144] In a possible implementation, the grayscale threshold of any i-th row of pixel sequences of the ground marker is detected to obtain a second pixel sequence of the ground marker; and the third grayscale distribution function of the ground marker is obtained based on the second grayscale distribution function of the second pixel sequences of each row of the ground marker.

[0145] In each row of pixel sequences of the ground marker, for any i-th row of pixel sequences, according to a fixed number of pixels on the first side and the second side of the second edge point of the i-th row of pixel sequences, the maximum grayscale value and the minimum grayscale value of the fixed number of pixels are obtained; if the value of the grayscale threshold of the i-th row of pixel sequences is between the maximum grayscale value and the minimum grayscale value of the fixed number of pixels, the i-th row of pixel sequences is taken as the second pixel sequence of the ground marker. Subsequently, according to the second grayscale distribution function of the second pixel sequences of each row of the ground marker, the third grayscale distribution function of the ground marker is obtained.

[0146] In one possible implementation, the second edge points of each row of pixel sequences are detected based on a random sampling consistency function to obtain a third pixel sequence of the ground marker; and the third grayscale distribution function of the ground marker is obtained based on the second grayscale distribution function of the third pixel sequence of each row of the ground marker.

[0147] In each row of pixel sequences of the ground marker, the position of the second edge point of each row of pixel sequences is obtained; based on the random sampling consistency function, it is determined whether the second edge point of any i-th row of pixel sequences deviates from the linear distribution of the second edge points of each row; specifically, it is determined whether the second edge point of the i-th row of pixel sequences is an outer point or an inner point; if the second edge point of the i-th row of pixel sequences is an inner point of the row edge, the i-th row of pixel sequences is used as the third pixel sequence of the ground marker. Subsequently, based on the second grayscale distribution function of the third pixel sequence of each row of the ground marker, the third grayscale distribution function of the ground marker is obtained by accumulating and averaging.

[0148] S1117, calculating and obtaining the MTF function of the ground marker according to the third grayscale distribution function of the ground marker.

[0149] Take the first derivative of the third grayscale distribution function of the ground marker to obtain the line spread function of the ground marker; then, perform Fourier transform on the line spread function of the ground marker to convert the line spread function of the ground marker from the spatial domain to the frequency domain; take the modulus of the result after Fourier transform as the MTF value of each frequency; take the first MTF value as the reference, normalize the MTF value of all frequencies to obtain the MTF function, so that the maximum value of the MTF function is 1; among which, the first MTF value is the value of the MTF when the frequency is 0; finally, truncate the MTF function according to the Nyquist cutoff frequency, and retain the information below the Nyquist frequency, and you can get the following: Figure 8 MTF function of the ground marker shown.

[0150] S113, calculating and obtaining a two-dimensional MTF matrix according to the MTF functions corresponding to the multiple ground markers.

[0151] According to the MTF function of the ground marker corresponding to the second straight line segment with the first angle of 0° and the MTF function of the ground marker corresponding to the second straight line segment with the first angle of 90°, a two-dimensional MTF matrix is ​​obtained. The two-dimensional MTF matrix is ​​used as a frequency domain filter to enhance the image.

[0152] For example, the two-dimensional MTF matrix can be calculated by the following formula 14.

[0153] MTF (u,v) =MTF u MTF v (14)

[0154] Among them, MTF u is the MTF function of the ground marker corresponding to the second straight line segment with a first angle of 0°, MTF v The MTF function of the ground marker corresponding to the second straight line segment with a first angle of 90°, MTF (u,v) is the MTF value at the two-dimensional frequency coordinate (u, v).

[0155] In a possible implementation, a two-dimensional Gaussian function model may be constructed using MTF functions obtained from a plurality of ground markers to simulate a two-dimensional MTF matrix.

[0156] Among them, the two-dimensional MTF matrix can be simulated according to the following formula:

[0157]

[0158]

[0159]

[0160] Among them, u and v are the horizontal and vertical coordinates in the two-dimensional MTF function respectively; MTF u 、MTF v are the one-dimensional MTF functions in the u and v directions respectively, A u , A v , A are the corresponding amplitudes in the MTF function, u0 and v0 are the coordinates of the maximum value of MTF in the MTF function; δ v , δ u is the standard deviation.

[0161] Taking the logarithm of both sides of the formula with base e, we get the following formula:

[0162]

[0163] Among them, MTF u 、MTF v are the one-dimensional MTF functions in the u and v directions respectively, A u , A v , A are the corresponding amplitudes in the MTF function, u0 and v0 are the coordinates of the maximum value of MTF in the MTF function; δ v , δ u is the standard deviation.

[0164] The formula is further simplified to obtain a formula, which simplifies the two-dimensional Gaussian model into a two-variable quadratic polynomial.

[0165] lnMTF (u,v) =au 2 +bv 2 +cu+dv+e (19)

[0166] in,

[0167] Among them, u and v are the coordinates of the two-dimensional function corresponding to the two-dimensional MTF matrix; MTF u 、MTF v are the one-dimensional MTF functions in the u and v directions respectively, A u , A v , A are the corresponding amplitudes in the MTF function, u0 and v0 are the coordinates of the maximum value of MTF in the MTF function; δ v , δ u is the standard deviation, a, b, c, d, e are the parameters to be determined.

[0168] With the goal of minimizing the residual sum of squares Q, the least squares method is used to solve formula 19, and the objective function is obtained as shown in formula 21; let the partial derivative of the objective function 21 with respect to each variable be zero, and obtain the equation group 22.

[0169] Q=min∑ m*n(au 2 +bv 2 +cu+dv+e-lnMTF) 2 (twenty one)

[0170]

[0171] Where Q is the residual sum of squares in the least squares method; m is the number of ground markers; n is the number of MTF sample values ​​used in the MTF function of a single ground marker; u and v are the coordinates of the two-dimensional function corresponding to the two-dimensional MTF matrix; δ v , δ u is the standard deviation; a, b, c, d, e are the parameters to be determined.

[0172] Solving the linear equation group 22 yields the parameters of the Gaussian function, thereby obtaining a two-dimensional MTF matrix.

[0173] For example, various parameters a, b, c, d, and e are calculated using the following formulas 23 and 24.

[0174]

[0175] K=B -1 C (24)

[0176] Among them, B is the coefficient matrix in formula 22; K is the parameter matrix in formula 22; C is the matrix of MTF values ​​in formula 22; u and v are the coordinates of the two-dimensional function corresponding to the two-dimensional MTF matrix; a, b, c, d, and e are the parameters to be determined respectively.

[0177] S115, using a two-dimensional MTF matrix to enhance the original image.

[0178] Perform Fourier transform on the original image, obtain the two-dimensional MTF matrix as the frequency domain filter, and then perform inverse Fourier transform on the image to obtain the edge enhanced image.

[0179] For example, Fig. 9 , 10 11 and 12 respectively show the comparison diagrams of the original images H, I, J, K and the images enhanced based on the original images H, I, J, K.

[0180] Corresponding to the above method provided by the present application, the present application also provides a deployment device 300. Fig.13 As shown, the device 300 includes: a line segment acquisition unit 301, an image determination unit 303, a coordinate calculation unit 305, a grayscale calculation unit 307, a function acquisition unit 309, and an image enhancement unit 311.

[0181] The line segment acquisition unit 301 is used to acquire a first straight line segment existing in the original image;

[0182] The image determination unit 303 obtains a second straight line segment from the first straight line segment according to the length of the first straight line segment, and determines the first image block from the original image according to the position of the second straight line segment in the original image;

[0183] A coordinate calculation unit 305 is used to rotate and / or translate the first image block to obtain a second image block, and obtain reference coordinates of each pixel in the first image block in the pixel coordinate system according to the pixel coordinate system of the second image block;

[0184] A grayscale calculation unit 307 is used to obtain a grayscale value of each pixel in the second image block according to the reference coordinates of each pixel in the first image block and the grayscale value of each pixel in the first image block; wherein the grayscale value of each pixel in the second image block is a sub-pixel grayscale value;

[0185] The function acquisition unit 309 is used to obtain the ground marker according to the grayscale mean value and the texture uniformity value of the second image block, and calculate the MTF function corresponding to the ground marker;

[0186] The image enhancement unit 311 is used to obtain a two-dimensional MTF matrix according to the MTF function, and enhance the original image using the two-dimensional MTF matrix.

[0187] According to another embodiment, there is also provided a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute a combination of Figure 1 The method described herein is performed by the line segment acquisition unit 301 , the image determination unit 303 , the coordinate calculation unit 305 , the grayscale calculation unit 307 , the function acquisition unit 309 , and the image enhancement unit 311 .

[0188] According to another embodiment of the present invention, a computing device is provided, including a memory and a processor. The memory stores executable code. When the processor executes the executable code, the Figure 1 The method described herein is performed by the line segment acquisition unit 301 , the image determination unit 303 , the coordinate calculation unit 305 , the grayscale calculation unit 307 , the function acquisition unit 309 , and the image enhancement unit 311 .

[0189] The above specific implementation methods further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above are only specific implementation methods of the present application and are not used to limit the protection scope of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present application should be included in the protection scope of the present application.

Claims

1. An image enhancement method, characterized in that: include: Obtain the first straight line segment existing in the original image; According to the length of the first straight line segment, a second straight line segment is obtained from the first straight line segment, and according to the position of the second straight line segment in the original image, a first image block is determined from the original image; specifically, the method includes: according to a predetermined length range, in the first straight line segment, a second straight line segment that meets a predetermined first length range is obtained; in the original image, according to the position of the second straight line segment in the original image, a rectangular image block with the second straight line segment as the center line is obtained, that is, the first image block; wherein a first side of the first image block is parallel to the second straight line segment and has the same length as the second straight line segment, and a second side of the first image block that is perpendicular to the first side is of a predetermined length; Rotate and / or translate the first image block to obtain a second image block, and obtain reference coordinates of each pixel in the first image block in the pixel coordinate system according to the pixel coordinate system of the second image block; Obtaining a grayscale value of each pixel in the second image block according to the reference coordinates of each pixel in the first image block and the grayscale value of each pixel in the first image block; wherein the grayscale value of each pixel in the second image block is a sub-pixel grayscale value; According to the grayscale mean and texture uniformity value of the second image block, a ground marker is obtained, and the MTF function corresponding to the ground marker is calculated, wherein the texture uniformity value is represented by an inverse difference, and the inverse difference is calculated according to the grayscale co-occurrence matrix GLCM of the second image block and the GLCM value of the pixel grayscale value pair in the GLCM; the calculation of the MTF function corresponding to the ground marker includes: for any i-th row of pixel sequences in the ground marker, according to the grayscale value of each pixel in the i-th row of pixel sequences, calculating the position of the second edge point in the i-th row of pixel sequences; according to the position of the second edge point in the i-th row of pixel sequences, obtaining the second grayscale distribution function of the i-th row of pixel sequences; accumulating and averaging the second grayscale distribution functions of each row of pixel sequences in the ground marker to obtain a third grayscale distribution function of the ground marker. distribution function; the MTF function of the ground marker is calculated according to the third grayscale distribution function of the ground marker; wherein, for any i-th row of pixel sequences in the ground marker, according to the grayscale values ​​of each pixel in the i-th row of pixel sequences, the position of the second edge point in the i-th row of pixel sequences is calculated, including: for any i-th row of pixel sequences in the ground marker, according to the grayscale values ​​of each pixel in the i-th row of pixel sequences, the first edge point in the i-th row of pixel sequences is obtained; according to the grayscale values ​​of each pixel in the i-th row of pixel sequences, the grayscale threshold of the i-th row of pixel sequences is calculated; the grayscale values ​​of a number of pixels on both sides of the first edge point are fitted to obtain the first grayscale distribution function of the i-th row of pixel sequences; the grayscale threshold is substituted into the first grayscale distribution function to obtain the position of the second edge point of the i-th row of pixel sequences A two-dimensional MTF matrix is ​​constructed according to the MTF function, and the original image is enhanced using the two-dimensional MTF matrix.

2. The method according to claim 1, wherein: The method of obtaining a ground marker based on the grayscale mean and texture uniformity value of the second image block includes: obtaining a ground marker that meets a preset regional grayscale difference threshold range and a preset texture uniformity threshold range from the second image block; wherein the texture uniformity value is represented by the IDM value of the second image block.

3. The method according to claim 1, wherein: Calculating the grayscale threshold of the i-th row of pixel sequence according to the grayscale value of each pixel in the i-th row of pixel sequence includes: calculating the grayscale threshold of the i-th row of pixel sequence by Otsu's method according to the grayscale value of each pixel in the i-th row of pixel sequence.

4. The method according to claim 1, wherein: The method of accumulating and averaging the second grayscale distribution function of each row of pixel sequences in the ground marker to obtain the third grayscale distribution function of the ground marker comprises: Detecting the grayscale threshold of any i-th row pixel sequence of the ground marker to obtain a second pixel sequence of the ground marker; The second grayscale distribution function of the second pixel sequence in each row of the ground marker is accumulated and averaged to obtain a third grayscale distribution function of the ground marker.

5. The method according to claim 1, wherein: The second grayscale distribution function of each row of pixel sequences in the ground marker is accumulated and averaged to obtain a third grayscale distribution function of the ground marker; comprising: Detecting the second edge points of each row of pixel sequences of the ground marker according to the random sampling consistency function to obtain a third pixel sequence of the ground marker; A third grayscale distribution function of the ground marker is obtained according to the second grayscale distribution function of the third pixel sequence in each row of the ground marker.

6. The method according to any one of claims 1 to 5, wherein: According to the MTF function corresponding to the ground marker, a two-dimensional MTF matrix is ​​calculated, including: according to the MTF function corresponding to the ground marker, a two-dimensional Gaussian function model is used for simulation to construct a two-dimensional MTF matrix.

7. An image enhancement device, characterized in that: include: A line segment acquisition unit, used for acquiring a first straight line segment existing in the original image; an image determination unit, configured to obtain a second straight line segment from the first straight line segment according to a length of the first straight line segment, and determine a first image block from the original image according to a position of the second straight line segment in the original image; a coordinate calculation unit, configured to rotate and / or translate the first image block to obtain a second image block, and obtain reference coordinates of each pixel in the first image block in the pixel coordinate system according to the pixel coordinate system of the second image block; a grayscale calculation unit, configured to obtain a grayscale value of each pixel in the second image block according to a reference coordinate of each pixel in the first image block and a grayscale value of each pixel in the first image block; wherein the grayscale value of each pixel in the second image block is a sub-pixel grayscale value; A function acquisition unit, used to obtain a ground marker according to the grayscale mean value and the texture uniformity value of the second image block, and calculate the MTF function corresponding to the ground marker; The image enhancement unit is used to obtain a two-dimensional MTF matrix according to the MTF function, and use the two-dimensional MTF matrix to enhance the original image.

8. A computing device, comprising a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method according to any one of claims 1 to 7 is implemented.

Images