Background noise suppression method for high-sensitivity image of space target

Abstract

The invention relates to the field of image denoising, in particular to a background noise suppression method for a high-sensitivity image of a space target, and the method comprises the steps: S1, carrying out the tracking imaging of the space target through optical measurement equipment, and obtaining a continuous high-sensitivity image sequence; s2, performing statistics on gray values in the row direction and the column direction in the high-sensitivity image sequence to obtain brightness distribution; s3, determining coordinates of a space target in the high-sensitivity image sequence based on the brightness distribution; s4, the exposure of a high-sensitivity camera in the optical measurement equipment is adjusted, the space target is imaged again, a 16-bit original image is obtained, and the brightness of pixel points around the space target in the original image reaches a brightness threshold value; s5, high 8-bit data selection and gray level equal-interval stretching are carried out on the 16-bit original image at the same time, and two 8-bit images are obtained; and S6, fusing the two 8-bit images to obtain a denoised high-sensitivity image. According to the invention, the noise of the high-sensitivity image of the space target is effectively removed.

Description

Technical Field

[0001] The present invention relates to the technical field of image denoising, and in particular to a method for suppressing background noise in a high-sensitivity image of a space target. Background Art

[0002] For high-sensitivity imaging of space targets, due to the long observation distance of space targets, they are often affected by factors such as atmospheric interference during imaging, resulting in weak imaging intensity and small imaging size of space targets. In high-sensitivity images, they mostly appear as tiny light spots, which are morphologically no different from the point noise of high-sensitivity cameras. This adds a lot of difficulties to the denoising of high-sensitivity images of space targets.

[0003] Numerous studies have been conducted on image denoising methods in the field of optical imaging, but most algorithms focus on denoising images from everyday life. However, in high-sensitivity images of space targets, the morphological differences between the space targets and noise are minimal. Therefore, traditional denoising methods remove these tiny space targets as noise. Furthermore, the high frame rate of optical measurement equipment places extremely high demands on the real-time performance of image denoising methods, posing significant challenges.

[0004] Traditional isotropic denoising methods primarily process the entire image uniformly through methods like median and mean filtering. This approach offers the advantage of real-time performance, but also has significant drawbacks. Because these denoising methods treat all pixels indiscriminately, the processed image loses detail, resulting in reduced sharpness and blurring. For high-sensitivity images of space targets, since the target's image size is only a few dozen pixels, it's virtually indistinguishable from noise signals. Therefore, after processing using these denoising methods, it is directly filtered out as noise.

[0005] Traditional anisotropic denoising methods, such as guided filtering, often first determine whether the high-frequency signals in the image belong to the image contour or noise, and then specifically remove these high-frequency signals based on the judgment results. Such methods can avoid the reduction in image sharpness and blurring caused by filtering the image contour area. However, in high-sensitivity images of space targets, the space target imaging is only a point without a clear contour, and the space target imaging effect is not much different from the background noise, making it difficult to determine the contour of the space target. Moreover, such methods are time-consuming and cannot meet the task requirements of background noise suppression in high-sensitivity images of space targets.

[0006] At present, with the development of deep learning technology, various deep learning denoising models have been established, and many deep learning denoising methods have achieved good results. However, there is still no dataset related to denoising of high-sensitivity imaging of space targets. Since the space targets and noise in high-sensitivity images of space targets are highly similar, it is even impossible to manually distinguish the space target areas and noise areas in the image when annotating the dataset. In addition, deep learning denoising methods are often time-consuming, so they are not suitable for background noise suppression of high-sensitivity images of space targets of optical measurement equipment with high real-time requirements. Summary of the Invention

[0007] In view of this, the present invention aims to provide a background noise suppression method for high-sensitivity images of space targets, so as to solve the technical problem that traditional image denoising methods are not applicable to background noise suppression of high-sensitivity images of space targets.

[0008] To achieve the above object, the technical solution created by the present invention is implemented as follows: A method for suppressing background noise of a high-sensitivity image of a space target comprises the following steps: S1: Use optical measurement equipment to track and image space targets to obtain continuous high-sensitivity image sequences; S2: Count the grayscale values ​​in the row and column directions of the high-sensitivity image sequence to obtain the brightness distribution; S3: Determine the coordinates of spatial targets in a high-sensitivity image sequence based on brightness distribution; S4: adjusting the exposure of the high-sensitivity camera in the light measurement device, re-imaging the space target, obtaining a 16-bit original image, and making the brightness of the pixels around the space target in the 16-bit original image reach the brightness threshold; S5: Simultaneously extract the high 8-bit data of the 16-bit original image and stretch the grayscale to equal intervals to obtain two 8-bit images; S6: Fuse the two 8-bit images to obtain a denoised high-sensitivity image.

[0009] Furthermore, suppose that the high-sensitivity image sequence includes n frames of high-sensitivity images, the width and height of each frame of high-sensitivity image are W and H respectively, and (x, y) is the horizontal coordinate and vertical coordinate of the pixel point in the high-sensitivity image; In step S2, the process of counting the grayscale values ​​in the row direction and the column direction in the high-sensitivity image sequence is as follows: Sum the grayscale values ​​of each row of the i-th frame high-sensitivity image to obtain the row grayscale value Img_i_line(y), Img_i_line(y)=Img_i(0,y)+Img_i(1,y)+…+Img_i(W-1,y), where Img_i(0,y), Img_i(1,y), …, and Img_i(W-1,y) represent the grayscale values ​​of pixel (0,y), pixel (1,y), …, and pixel (W-1,y) in the i-th frame high-sensitivity image, respectively. Sum the grayscale values ​​of each column of the i-th frame high-sensitivity image to obtain the column grayscale value Img_i_column(x), Img_i_column(x)=Img_i(x,0)+Img_i(x,1)+…+Img_i(x,H-1), where Img_i(x,0), Img_i(x,1), …, and Img_i(x,H-1) represent the grayscale values ​​of pixel (x,0), pixel (x,1), …, and pixel (x,H-1) in the i-th frame high-sensitivity image, respectively. Sum the grayscale values ​​of each row of the n-frame high-sensitivity image to obtain the total grayscale value Img_line(y), Img_line(y)=Img_1_line(y)+Img_2_line(y)+…+Img_n_line(y), where Img_1_line(y), Img_2_line(y), …, and Img_n_line(y) represent the grayscale values ​​of each row of the high-sensitivity image frames 1 to n, respectively. Sum the column grayscale values ​​of each column of n frames of high-sensitivity images to obtain the total column grayscale value Img_column(x), Img_column(x)=Img_1_column(x)+Img_2_column(x)+…+Img_n_column(x), where Img_1_column(x), Img_2_column(x), …, Img_n_column(x) represent the column grayscale values ​​of each column of the high-sensitivity images from the 1st frame to the nth frame, respectively.

[0010] Furthermore, let the coordinates of the space target in the high-sensitivity image be (xn,yn); In step S3, the process of determining the coordinates of the space target in the high-sensitivity image sequence based on the brightness distribution is as follows: According to the grayscale value, count the top 0.5% of the pixels in the column grayscale total value Img_column(x) and the row grayscale total value Img_line(y), and remove the discontinuous pixels in the statistical results to obtain the horizontal coordinate set {x1,x2,…xa} and the vertical coordinate set {y1,y2,…yb}, then: xn=(x1+x2+…+xa) / a; yn=(y1+y2+…+yb) / b.

[0011] Furthermore, in step S4, the brightness threshold is set to 50,000.

[0012] Furthermore, assuming that the 16-bit original image is Img0, and the grayscale value of the pixel (x, y) in the original image Img0 is Img0(x, y), then in step S5, the high-order 8-bit data of the original image Img0 is selected, and the obtained 8-bit image is Img1. The grayscale value Img0(x, y) of the pixel (x, y) in the 8-bit image Img1 is converted to Img1(x, y) by the following formula:

[0013] Furthermore, in step S5, the process of performing grayscale equal-interval stretching on the original image Img0 is as follows: Count the grayscale values ​​of all pixels in the original image Img0 to obtain a set of pixels in the grayscale value range of 0-65535; Count the number of valid pixels in the pixel set, record it as count, and record them in ascending order; The original image Img0 is transformed to obtain an 8-bit image Img2. The grayscale value Img0(x,y) of the pixel (x,y) in the 8-bit image Img2 is converted to Img2(x,y) by the following formula:

[0014] Wherein, t is the ranking of the grayscale value Img0(x,y) of the pixel (x,y) in the original image Img0 in the valid grayscale values ​​in ascending order.

[0015] Furthermore, in step S6, the two 8-bit images are fused to obtain a denoised high-sensitivity image Img3. The grayscale value of the high-sensitivity image Img3 is Img3(x,y), and the calculation formula is:

[0016] Among them, μ is the fusion parameter.

[0017] Furthermore, after step S6, the following steps are further included: S7: Perform stretching processing on the high-sensitivity image Img3 using a stretching method.

[0018] Furthermore, the stretching method is a linear stretching method or a nonlinear stretching method.

[0019] Compared with the prior art, the present invention can achieve the following beneficial effects: (1) The noise suppression speed is fast, taking milliseconds to complete, which can meet the high real-time requirements of optical measurement equipment; (2) Based on the characteristics that space target imaging is highly sensitive to camera dimming, while noise is less sensitive to camera dimming, the problem of difficulty in distinguishing between space targets and noise due to their similar morphology is effectively solved, thereby avoiding the situation where tiny space targets are treated as noise and removed together with the noise.

[0020] (3) It can appropriately protect the background while suppressing noise, and ensure that faint stars are not removed after imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings: Figure 1 A schematic flow chart of a method for suppressing background noise in a high-sensitivity image of a space target according to an embodiment of the present invention; Figure 2 A schematic diagram of a nonlinear processing function according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a high-sensitivity image before denoising according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a high-sensitivity image after denoising according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

[0023] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0024] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying 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 invention. In addition, the terms "first", "second" and the like are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, features defined as "first", "second" and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0025] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art can understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0026] Because the morphological differences between the images of space targets and noise in high-sensitivity images of space targets are minimal, tiny space targets are easily filtered out as noise. To distinguish space targets from noise in high-sensitivity images, the present invention achieves this distinction based on the different response characteristics of space targets and noise to the dimming of a high-sensitivity camera used by a photometric device. The image of a space target is highly sensitive to camera dimming, while the image of noise is less sensitive. Therefore, when dimming the high-sensitivity camera, the image of the space target can be adjusted to a brightness close to overexposure. That is, for a 16-bit high-sensitivity image, the grayscale value of the image of the space target can be adjusted to close to 60,000, while the brightness of the noise at this level is lower and often cannot reach higher brightness levels. When the image of the space target is close to overexposure, the grayscale value of the noise is typically less than 7,000, while the grayscale value of the background is even lower, typically not exceeding 2,000. The present invention utilizes this prior knowledge to achieve noise removal.

[0027] The following will refer to Figures 1 to 4 The present invention is described in detail with reference to the embodiments.

[0028] like Figure 1As shown, an embodiment of the present invention provides a method for suppressing background noise in a high-sensitivity image of a space target, comprising the following steps: S1: Use optical measurement equipment to track and image space targets to obtain continuous high-sensitivity image sequences.

[0029] For space target observation missions, the target's trajectory is known. During this mission, the optical measurement equipment is controlled to track the target based on its trajectory. The target remains within the optical measurement field of view and in a relatively stable position in the high-sensitivity image. Therefore, the target's position in the high-sensitivity image can be considered unchanged.

[0030] The high-sensitivity camera of the optical measurement equipment continuously shoots the space target to obtain n frames of continuous high-sensitivity images.

[0031] S2: Count the grayscale values ​​in the row and column directions of the high-sensitivity image sequence to obtain the brightness distribution.

[0032] Assume that the width and height of each frame of high-sensitivity image are W and H, the grayscale value of the pixel point (x, y) is Img_i(x, y), x is the horizontal coordinate of the pixel point in the high-sensitivity image, and y is the vertical coordinate of the pixel point in the high-sensitivity image.

[0033] First, the grayscale values ​​in the row and column directions of each consecutive high-sensitivity image are counted, and the grayscale values ​​in the row and column directions are counted row by row and column by column.

[0034] Take the i-th frame of high-sensitivity image as an example: The grayscale values ​​of the y-th row of the i-th frame high-sensitivity image are summed to obtain the row grayscale value Img_i_line(y).

[0035] Img_i_line(y)=Img_i(0,y)+Img_i(1,y)+…+Img_i(W-1,y); where Img_i(0,y), Img_i(1,y), …, and Img_i(W-1,y) represent the grayscale values ​​of pixel (0,y), pixel (1,y), …, and pixel (W-1,y) in the i-th frame of the high-sensitivity image, respectively.

[0036] The grayscale value statistics of other rows of the i-th frame high-sensitivity image can be obtained similarly.

[0037] The grayscale values ​​of the x-th column of the i-th frame high-sensitivity image are summed to obtain the column grayscale value Img_i_column(x).

[0038] Img_i_column(x)=Img_i(x,0)+Img_i(x,1)+…+Img_i(x,H-1); where Img_i(x,0), Img_i(x,1), …, and Img_i(x,H-1) represent the grayscale values ​​of pixel (x,0), pixel (x,1), …, and pixel (x,H-1) in the i-th high-sensitivity image, respectively.

[0039] The grayscale value statistics of other columns of the i-th frame high-sensitivity image can be obtained similarly.

[0040] Secondly, the row grayscale values ​​of all high-sensitivity images are summed to obtain the total row grayscale value, and the row grayscale values ​​of all high-sensitivity images are summed to obtain the total column grayscale value.

[0041] Take the pixel point (x, y) as an example: Sum the row grayscale values ​​of the yth row of the n-frame high-sensitivity image to obtain the total row grayscale value Img_line(y).

[0042] Img_line(y)=Img_1_line(y)+Img_2_line(y)+…+Img_n_line(y); wherein Img_1_line(y), Img_2_line(y), …, Img_n_line(y) represent the grayscale values ​​of the y-th line of the high-sensitivity images of the 1st to nth frames, respectively.

[0043] The same can be done for summing the grayscale values ​​of other rows of n frames of high-sensitivity images.

[0044] Sum the column grayscale values ​​of the x-th column of the n-frame high-sensitivity image to obtain the total column grayscale value Img_column(x).

[0045] Img_column(x)=Img_1_column(x)+Img_2_column(x)+…+Img_n_column(x); wherein Img_1_column(x), Img_2_column(x), …, Img_n_column(x) represent the column grayscale values ​​of the x-th column of the high-sensitivity images of the 1st to nth frames, respectively.

[0046] The same can be done for summing the grayscale values ​​of other columns of n frames of high-sensitivity images.

[0047] Through the above-mentioned row and column grayscale value statistics, the brightness distribution of the high-sensitivity image is obtained, which is convenient for the subsequent distinction between spatial targets and noise.

[0048] Since the space target is a bright target, the grayscale value of its image is relatively high. Assuming the coordinates of the space target in the high-sensitivity image are (xn,yn), it will inevitably lead to the sum of the grayscale values ​​of Img_i_line(xn), Img_i_column(yn) and its adjacent row and column positions being significantly higher than the sum of the grayscale values ​​of other positions.

[0049] It should be noted that (xn,yn) represents a plurality of pixel points occupied by the space target in the high-sensitivity image, n is an independent variable, xn represents the horizontal coordinate of the pixel point occupied by the space target, and yn represents the vertical coordinate of the pixel point occupied by the space target.

[0050] S3: Determine the coordinates of the spatial target in the high-sensitivity image sequence based on the brightness distribution.

[0051] According to the grayscale value, count the top 0.5% of the pixels in the column grayscale total value Img_column(x) and the row grayscale total value Img_line(y), and observe the values ​​of x and y. Remove the discontinuous pixels in the statistical results to obtain the horizontal coordinate set {x1, x2, ...xa} and the vertical coordinate set {y1, y2, ...yb}, then: xn=(x1+x2+…+xa) / a; yn=(y1+y2+…+yb) / b.

[0052] The purpose of the above operation is to select the rows and columns where the brightest pixels in the high-sensitivity image are located. This is measured by calculating the average grayscale value of the pixel rows and columns. Specifically, the operation is to select the top 0.5% of the brightest pixels in the total column grayscale value Img_column(x) and the total row grayscale value Img_line(y) for grayscale value statistics. Because infrared images often have some bad pixels like snowflakes, these noises often only occupy one or two pixels, but the grayscale values ​​are often relatively high (similar to white dots). Discontinuous x and y values ​​represent this noise. Therefore, removing discontinuous pixels from the statistical results can eliminate the interference of this noise on the grayscale value statistics.

[0053] The mean of all grayscale values ​​in a row or column of n consecutive high-sensitivity images is calculated to measure whether there is a space target in this row or column. Since the grayscale value of a space target is brighter, if there is a space target, the mean grayscale value of this row or column will be higher, thereby obtaining the row coordinates and vertical coordinates of the space target, and then quickly locating the position of the space target in the high-sensitivity image.

[0054] S4: Adjust the exposure of the high-sensitivity camera in the light measuring device, re-image the space target, and obtain a 16-bit original image, so that the brightness of the pixels around the space target in the 16-bit original image reaches the brightness threshold.

[0055] By adjusting the exposure of the high-sensitivity camera and the brightness of the high-sensitivity image, the grayscale values ​​of the pixels around the spatial target (xn, yn) are brought close to saturation. This invention uses a 16-bit high-sensitivity image with a saturated grayscale value of 65535 and a brightness threshold of 50000. The high-sensitivity camera exposure is controlled so that the brightness of the four pixel regions above, below, left, and right around the spatial target coordinate point (xn, yn) is all above 50000. At this point, the image of the spatial target in the high-sensitivity image is brighter, and the image of the noise is also enhanced, but the brightness of the noise portion is relatively low compared to the brightness of the spatial target.

[0056] S5: Simultaneously, the high 8-bit data of the 16-bit original image is selected and the grayscale is stretched at equal intervals to obtain two 8-bit images.

[0057] (1) Select the high 8-bit data of the 16-bit original image Select the high-order 8-bit data of the 16-bit original image to obtain an 8-bit single-channel image. Due to the brightness difference between the space target and the noise, the space target can still be clearly imaged in the high-order 8-bit data, but the noise information will be completely filtered out due to its lower brightness. However, at the same time, the images of some faint celestial bodies may also be suppressed. Let the 16-bit original image be Img0, and the grayscale value at the pixel point (x, y) in the original image Img0 be recorded as Img0(x, y). After taking the high-order 8-bit data, the obtained 8-bit single-channel image is Img1, and the grayscale value at the pixel point (x, y) in the single-channel image Img1 becomes Img1(x, y):

[0058] (2) Perform grayscale equal spacing stretching on the 16-bit original image First, the grayscale values ​​of all pixels in the original image Img0 are counted to obtain a set of pixels in the grayscale value range of 0-65535.

[0059] Then, count the number of valid grayscale values ​​within the pixel set, denoted as count, and record these valid grayscale values ​​in ascending order. A valid grayscale value is defined as follows: traverse the image point by point, count a grayscale value val in the range of 0-65535, and determine whether there is a pixel with grayscale value val in the high-sensitivity image. If so, val is considered a valid grayscale value; otherwise, val is considered an invalid grayscale value.

[0060] At this time, the pixels in the original image Img0 are transformed to generate an 8-bit single-channel image Img2: the grayscale value at the pixel point (x, y) in the single-channel image Img2 becomes Img2(x, y). Suppose in the original image Img0, if Img0(x, y) ranks t among all valid grayscale values ​​in ascending order, then:

[0061] S6: Fuse the two 8-bit images to obtain a denoised high-sensitivity image.

[0062] Since both the single-channel image Img1 and the single-channel image Img2 are 8-bit images, the two are fused to obtain the denoised high-sensitivity image Img3.

[0063] The grayscale value of the high-sensitivity image Img3 is Img3(x,y), and the calculation formula is:

[0064] Among them, μ is the fusion parameter, which is controlled by manual setting.

[0065] Since the method of the present invention does not involve complex matrix operations or data transmission between the GPU, the method itself is less time-consuming, taking only milliseconds, ensuring a fast noise suppression speed and meeting the high real-time requirements of optical measurement equipment.

[0066] After step S6, the following steps are also included: S7: Perform stretching processing on the high-sensitivity image Img3 using a stretching method.

[0067] The noise in the denoised high-sensitivity image Img3 has been weakened to a certain extent. By stretching the high-sensitivity image Img3, the grayscale difference between the spatial target and the noise can be further widened.

[0068] The stretching method may be a linear stretching method or a nonlinear stretching method.

[0069] Taking the nonlinear stretching method as an example, the nonlinear processing function used by the nonlinear stretching method is as follows: Figure 2 As shown, the transformation function curve selected in the present invention is represented by y=f(x), which is an S-shaped curve. Figure 2From the analysis, we can see that when the grayscale value x in the image before the transformation is high, the transformed result f(x) is greater than x. When the grayscale value x in the image before the transformation is low, the transformed result f(x) is less than x. That is, after this transformation, the grayscale value of the bright part of the image where the spatial target is located increases, making the corresponding white area whiter, while the grayscale value of the background and noise area in the image decreases, making the corresponding black area darker. This further suppresses noise.

[0070] The final image Img_dst after processing is:

[0071] At this point, the final denoised image Img_dst can be obtained. Figure 3 is the image effect before denoising, Figure 4 This is the image effect after denoising.

[0072] After image fusion, the noise will be suppressed, the noise intensity will be weakened, and it will be closer to the background grayscale. By stretching the image, the difference between light and dark will be more obvious, so the noise will be further suppressed.

[0073] The present invention utilizes a method in which there is a certain brightness difference between the space target and the image background. First, the exposure is adjusted by a high-sensitivity camera to increase the brightness of the high-sensitivity image. At this time, the brightness difference between the space target and the image background will become larger. Then, by selecting the high eight-bit data, the noise in the background is removed to complete the initial suppression of the noise. After that, the two 8-bit images are weighted fused, and the background information of the fused image is restored. Finally, the image is stretched to further enhance the brighter space target and weaken the background noise. Dim targets such as faint stars have a certain brightness difference with the background. Through image fusion and stretching, they will be retained on the background and will not be completely removed, while the background noise will be suppressed.

[0074] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved. This is not limited herein.

[0075] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for suppressing background noise of a high-sensitivity image of a space target, characterized in that: The steps include: S1: Use optical measurement equipment to track and image space targets to obtain continuous high-sensitivity image sequences; S2: Count the grayscale values ​​in the row and column directions in the high-sensitivity image sequence to obtain the brightness distribution; S3: Determine the coordinates of the space target in the high-sensitivity image sequence based on the brightness distribution; S4: adjusting the exposure of the high-sensitivity camera in the optical measurement device, re-imaging the space target, obtaining a 16-bit original image, and making the brightness of the pixel points around the space target in the 16-bit original image reach the brightness threshold; S5: simultaneously select the high 8-bit data of the 16-bit original image and stretch the grayscale with equal spacing to obtain two 8-bit images; S6: Fuse the two 8-bit images to obtain a denoised high-sensitivity image.

2. The method for suppressing background noise of a space target high-sensitivity image according to claim 1, characterized in that: Assume that the high-sensitivity image sequence includes n frames of high-sensitivity images, the width and height of each frame of high-sensitivity image are W and H respectively, and (x, y) is the horizontal coordinate and vertical coordinate of the pixel point in the high-sensitivity image; In step S2, the process of counting the grayscale values ​​in the row direction and the column direction in the high-sensitivity image sequence is as follows: Sum the grayscale values ​​of each row of the i-th frame high-sensitivity image to obtain the row grayscale value Img_i_line(y), Img_i_line(y)=Img_i(0,y)+Img_i(1,y)+…+Img_i(W-1,y), Img_i(0,y), Img_i(1,y), …, Img_i(W-1,y) respectively represent the grayscale values ​​of pixel (0,y), pixel (1,y), …, pixel (W-1,y) in the i-th frame high-sensitivity image; Sum the grayscale values ​​of each column of the i-th frame of high-sensitivity image to obtain the column grayscale value Img_i_column(x), Img_i_column(x)=Img_i(x,0)+Img_i(x,1)+…+Img_i(x,H-1), where Img_i(x,0), Img_i(x,1), …, Img_i(x,H-1) represent the grayscale values ​​of pixel (x,0), pixel (x,1), …, pixel (x,H-1) in the i-th frame of high-sensitivity image, respectively; Sum the grayscale value of each line of the n-frame high-sensitivity image to obtain the total grayscale value Img_line(y), Img_line(y)=Img_1_line(y)+Img_2_line(y)+…+Img_n_line(y), Img_1_line(y), Img_2_line(y), …, Img_n_line(y) represent the grayscale value of each line of the 1st to the nth frame high-sensitivity images respectively; The column grayscale values ​​of each column of n frames of high-sensitivity images are summed to obtain the total column grayscale value Img_column(x), Img_column(x)=Img_1_column(x)+Img_2_column(x)+…+Img_n_column(x), Img_1_column(x), Img_2_column(x), …, Img_n_column(x) represent the column grayscale values ​​of each column of the 1st to nth frames of high-sensitivity images, respectively.

3. The method for suppressing background noise of a space target high-sensitivity image according to claim 2, characterized in that: Assume that the coordinates of the space target in the high-sensitivity image are (xn,yn); In step S3, the process of determining the coordinates of the space target in the high-sensitivity image sequence based on the brightness distribution is as follows: According to the gray value, the top 0.5% of the pixels in the total gray value of the column Img_column(x) and the total gray value of the row Img_line(y) are counted, and the discontinuous pixels in the statistical results are removed to obtain the abscissa set {x1,x2,…xa} and the ordinate set {y1,y2,…yb}, then: xn=(x1+x2+…+xa) / a; yn=(y1+y2+…+yb) / b.

4. The method for suppressing background noise of a space target high-sensitivity image according to any one of claims 1 to 3, characterized in that: In step S4, the brightness threshold is set to 50,000.

5. The method for suppressing background noise of a space target high-sensitivity image according to claim 4, characterized in that: Assume that the 16-bit original image is Img0, and the grayscale value of the pixel (x, y) in the original image Img0 is Img0(x, y). Then in step S5, the high 8-bit data of the original image Img0 is selected, and the obtained 8-bit image is Img1. The grayscale value Img0(x, y) of the pixel (x, y) in the 8-bit image Img1 is converted to Img1(x, y) by the following formula:

6. The method for suppressing background noise of a space target high-sensitivity image according to claim 5, characterized in that: In step S5, the process of stretching the original image Img0 with equal spacing in grayscale is as follows: The grayscale values ​​of all pixels in the original image Img0 are counted to obtain a set of pixels in the grayscale value range of 0-65535; Count the number of valid pixels in the pixel set, record it as count, and record it in ascending order; The original image Img0 is transformed to obtain an 8-bit image Img2. The grayscale value Img0(x,y) of the pixel (x,y) in the 8-bit image Img2 is converted to Img2(x,y) by the following formula: Among them, t is the ranking of the gray value Img0(x,y) of the pixel (x,y) in the original image Img0 in the valid gray values ​​in ascending order.

7. The method for suppressing background noise of a space target high-sensitivity image according to claim 6, characterized in that: In step S6, the two 8-bit images are fused to obtain a denoised high-sensitivity image Img3. The grayscale value of the high-sensitivity image Img3 is Img3(x, y), and the calculation formula is: Among them, μ is the fusion parameter.

8. The method for suppressing background noise of a space target high-sensitivity image according to claim 7, characterized in that: After step S6, the method further includes the following steps: S7: Use a stretching method to stretch the high-sensitivity image Img3.

9. The method for suppressing background noise of a space target high-sensitivity image according to claim 8, characterized in that: The stretching method is a linear stretching method or a nonlinear stretching method.

Images