Equipment miniaturization method based on signal processing

Abstract

The invention relates to the technical field of image data processing, in particular to an equipment miniaturization method based on signal processing, which comprises the following steps of: determining dynamic pixel areas and determining a marking mode of feature recognition points of each dynamic pixel area in an image; determining a first dynamic parameter according to the position change of the feature recognition points in the adjacent frame images; determining a second dynamic parameter through the included angle between the dynamic representation unit vectors in the distribution marking mode; and determining a denoising mode for the continuous frame images according to the parameter correlation degree of the first dynamic parameter and the second dynamic parameter, so as to construct correlation between overall features and individual features for the dynamic target object in the satellite image, screen the image with higher signal characterization for efficient processing, reduce design redundancy of receiving equipment, and improve the receiving efficiency. And the requirements of light weight, convenience and high-efficiency data processing of equipment in practical application are met.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a device miniaturization method based on signal processing. Background Art

[0002] In recent years, with the rapid development of satellite remote sensing technology, large-scale continuous frame image data needs to be processed efficiently. The traditional image reception and processing method often processes all scanned image data comprehensively and to the same extent. This not only consumes a lot of equipment resources, including storage space, computing power, etc., but also leads to large equipment size and increased energy consumption, which is difficult to meet the needs of miniaturization and lightweight equipment in practical applications. In addition, the images in the satellite scanning area contain a lot of information, including both dynamically changing parts and relatively static parts. If these information with different characteristics cannot be effectively distinguished and processed in a targeted manner, the equipment will do a lot of unnecessary work during the processing process.

[0003] Accurately extracting dynamic information and how to reasonably process and receive data based on this dynamic information in order to minimize the amount of data, reduce the equipment processing burden, and achieve equipment miniaturization while ensuring that key image information is not lost are issues that need to be addressed urgently.

[0004] For example, China's patent application publication number: CN117011193A, the invention discloses a lightweight staring satellite video denoising method and denoising system, the denoising method includes: obtaining the first pixel coordinates of the same pixel in each frame image of the video stream in time sequence to form a first pixel coordinate sequence of the same pixel; performing curve fitting based on multiple first pixel coordinates in the first pixel coordinate sequence of the same pixel to obtain the second pixel coordinate of each first pixel coordinate in the first pixel coordinate sequence on the fitting curve; judging whether to replace the first pixel coordinate with the second pixel coordinate according to the difference between the first pixel coordinate and the corresponding second pixel coordinate, so as to realize denoising processing for each pixel in each frame image.

[0005] The prior art still has the following problems: The existing technology does not consider the association between the overall characteristics and individual characteristics of dynamic targets in satellite images, the screening of more signal representative images for efficient processing, the reduction of design redundancy of receiving equipment, and the satisfaction of the needs of lightweight, convenient equipment and efficient data processing in practical applications. Summary of the invention

[0006] To this end, the present invention provides a device miniaturization method based on signal processing to overcome the problems that the prior art cannot establish the association between the overall characteristics and individual characteristics of dynamic targets in satellite images, cannot screen images with more signal representation for efficient processing, causes design redundancy of the receiving device, and affects the miniaturization effect of the receiving device.

[0007] To achieve the above object, the present invention provides a device miniaturization method based on signal processing, comprising: Acquire several consecutive frames of satellite images in the scanning area in advance, and determine the dynamic pixel area based on the image difference between adjacent frames; Determine a marking method for feature identification points of each dynamic pixel region in the image according to the area of ​​each dynamic pixel region, wherein the marking method includes an overall marking method or a distributed marking method; In the overall marking mode, the first dynamic parameter is determined according to the position change of the feature recognition point of the dynamic pixel area in the adjacent frame images; In a distributed marking mode, a plurality of dynamic characterization unit vectors are constructed according to the positions of each feature recognition point of the dynamic pixel region in adjacent frame images, and a second dynamic parameter is determined according to the angle between each dynamic characterization unit vector and the remaining dynamic characterization unit vectors; Determining a denoising method for continuous frame images according to a parameter correlation between a first dynamic parameter and a second dynamic parameter in a plurality of frame images; The denoising method includes screening a number of received frames and adjusting the duration of denoising processing for each dynamic pixel region in the image of the received frame; The amount of data corresponding to the image of the received frame after denoising is determined, and the data storage redundancy of the image receiving device is determined according to the amount of data.

[0008] Further, the process of determining the dynamic pixel area based on the image difference of adjacent frames includes: Divide the continuous frame images equally into several sub-regions; Determine the pixel value difference of each sub-region in two adjacent frames; The sub-regions whose pixel value differences exceed a preset pixel value difference threshold are determined as dynamic pixel sub-regions, and the region composed of mutually adjacent dynamic pixel sub-regions is determined as a dynamic pixel region.

[0009] Furthermore, the marking method of determining the feature recognition points of each dynamic pixel area in the image includes: Acquire the area of ​​each dynamic pixel region, and if the area satisfies the feature distinguishing condition, determine that the marking method of the feature identification points of the dynamic pixel region is a distributed marking method; If the area does not meet the feature distinction condition, the marking method of the feature identification points of the dynamic pixel area is determined to be an overall marking method; The feature distinguishing condition is that the area exceeds the average area of ​​several dynamic pixel regions in the image.

[0010] Furthermore, the overall marking method is to mark the centroid of the area contour of the dynamic pixel area as a feature recognition point; The distribution marking method includes determining the edge of the area contour of the dynamic pixel area, and setting a plurality of feature recognition points along the edge of the area contour at a preset interval.

[0011] Furthermore, the process of determining the first dynamic parameter is: The position change distance is determined based on the position of the feature recognition points of each dynamic pixel area in adjacent frame images, and the average value of several position change distances is determined as the first dynamic parameter.

[0012] Furthermore, the process of determining the second dynamic parameter includes: The positions of each feature recognition point of the dynamic pixel area in the adjacent frame images are respectively used as the vector starting point and the vector end point, and a dynamic representation unit vector is constructed according to the vector starting point and the vector end point; Determine the dynamic characterization unit vector corresponding to each feature recognition point on the edge of the area contour of the dynamic pixel area; The average value of the angles between each dynamic characterization unit vector and the remaining dynamic characterization unit vectors is determined as the second dynamic parameter.

[0013] Further, the process of determining the parameter correlation between the first dynamic parameter and the second dynamic parameter includes: The first dynamic parameter and the second dynamic parameter of each frame image are acquired frame by frame in a time sequence, and the Pearson coefficient of the first dynamic parameter and the second dynamic parameter is determined as the parameter correlation.

[0014] Furthermore, the process of screening the received frames is to determine the parameter correlation of a number of consecutive frame images in time sequence, and if the difference between the parameter correlation and a preset parameter correlation reference value meets the screening condition, the current frame corresponding to the parameter correlation is screened as the received frame; The screening condition is that the difference between the parameter correlation and a preset parameter correlation reference value is less than a preset difference threshold.

[0015] Furthermore, in the denoising process, the duration of adjusting the denoising process in each dynamic pixel region is negatively correlated with the difference threshold.

[0016] Furthermore, the data storage redundancy is positively correlated with the data volume.

[0017] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention determines the dynamic pixel area through the image difference between adjacent frames in the continuous frame image; and determines the marking method of the feature identification points of each dynamic pixel area in the image, and under the overall marking method, determines the first dynamic parameter according to the position change of the feature identification points in the adjacent frame image; under the distributed marking method, determines the second dynamic parameter by the angle between the dynamic characterization unit vectors; and determines the denoising method for the continuous frame image according to the parameter correlation between the first dynamic parameter and the second dynamic parameter, thereby realizing the association between the overall characteristics and individual characteristics of the dynamic target in the satellite image, screening images with more signal representation for efficient processing, reducing the design redundancy of the receiving equipment, and meeting the requirements of lightweight, convenient and efficient data processing of equipment in practical applications.

[0018] Furthermore, the present invention can accurately capture the changed parts of the image by equally dividing the continuous frame images into sub-regions and then calculating the difference in pixel values ​​of each sub-region in two adjacent frames. It can be understood that the manifestation of dynamic targets in the image is often the change of pixel values. This method of calculating the difference can effectively filter out these dynamic changes from the entire image. At the same time, adjacent dynamic pixel sub-regions are combined into dynamic pixel regions, which can completely outline the contours of the dynamic targets. By screening the dynamic pixel regions, indiscriminate processing of all pixels in the entire image can be avoided, thereby realizing the screening of dynamic targets in satellite images.

[0019] Furthermore, the present invention selects different feature identification point marking methods according to the comparison results between the dynamic pixel area and the average area of ​​several dynamic pixel areas, so that the marking method is more in line with the actual characteristics of the area. It can be understood that for a smaller area, the overall marking method uses the centroid of the area contour as the feature identification point, which can clearly reflect its overall position movement, approximate direction change and other macroscopic dynamic characteristics. For a larger area, the distributed marking method sets feature identification points along the edge of the contour, which can accurately capture the detailed dynamic changes of the area, thereby realizing the classification of dynamic targets in satellite images and avoiding the waste of resources caused by the use of complex markings in small areas.

[0020] Furthermore, the present invention determines the first dynamic parameter by calculating the average value of the position change distance of the feature identification points of each dynamic pixel area in adjacent frame images, which intuitively reflects the overall movement of the dynamic pixel area. By constructing a dynamic characterization unit vector and calculating the average value of the angle between each vector, the morphological change of the dynamic pixel area can be captured. By acquiring the first dynamic parameter and the second dynamic parameter frame by frame in time sequence and calculating their Pearson coefficient as the parameter correlation, the potential relationship between the first dynamic parameter and the second dynamic parameter can be excavated. By analyzing the relationship between the first dynamic parameter and the second dynamic parameter, a model for checking the consistency of image data can be established. It can be understood that if the relationship between the first dynamic parameter and the second dynamic parameter fluctuates significantly in a certain frame or a certain area, it represents that there are obvious errors in the image data or the noise interference is too large. Furthermore, by constructing the association between the overall characteristics and individual characteristics of the dynamic target in the satellite image, images with more signal representation are screened for efficient processing, the design redundancy of the receiving device is reduced, and the requirements for lightweight, convenient and efficient data processing of the equipment in practical applications are met.

[0021] Furthermore, the present invention can accurately avoid image frames with errors or excessive noise interference by determining the parameter correlation of consecutive frame images in time sequence and screening the received frames based on the difference with the preset parameter correlation reference value. The screened received frames are more representative, and thus, it is possible to screen images with more signal representation for efficient processing, reduce the design redundancy of the receiving device, and meet the needs of lightweight, convenient and efficient data processing in practical applications.

[0022] Furthermore, the present invention adjusts the duration of the denoising process so that the denoising process can flexibly allocate resources according to actual conditions. It can be understood that the smaller the deviation between the current frame parameter correlation and the parameter correlation reference value, the more consistent the dynamic characteristics of the current frame are with expectations, and the dynamic characteristics in the image may be more significant. At this time, increasing the denoising duration can effectively remove noise from images with signal representation and more effectively retain key information in the image. Combined with the screening of received frames and the adaptive denoising duration adjustment, the optimal allocation of system resources is achieved. The screening of received frames reduces the number of frames that need to be processed, and the adjustment of the denoising duration according to the difference threshold ensures that the computing resources can be reasonably used when processing each frame. Furthermore, it is achieved that images with more signal representation are screened for efficient processing, reducing the design redundancy of the receiving device, and meeting the requirements of lightweight, convenient and efficient data processing in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A step diagram of a device miniaturization method based on signal processing according to an embodiment of the present invention; Figure 2A diagram showing the steps of determining a dynamic pixel area according to an embodiment of the present invention; Figure 3 A logic flow chart for determining a marking method of a feature recognition point according to an embodiment of the present invention; Figure 4 A diagram showing the steps of determining a second dynamic parameter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0026] It should be noted that, in the description of the present invention, terms such as "upper", "lower", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0027] See also Figure 1 As shown, it is a step diagram of a device miniaturization method based on signal processing according to an embodiment of the present invention. The device miniaturization method based on signal processing according to the present invention comprises: Step S10, pre-acquire a number of continuous frame images of the satellite in the scanning area, and determine the dynamic pixel area based on the image difference of adjacent frames; Step S20, determining a marking method of feature identification points of each dynamic pixel region in the image according to the area of ​​each dynamic pixel region, wherein the marking method includes an overall marking method or a distributed marking method; Step S30, determining a first dynamic parameter according to a position change of a feature recognition point in a dynamic pixel region in adjacent frame images in an overall marking mode; Step S40, in a distributed marking mode, constructing a plurality of dynamic characterization unit vectors according to the positions of each feature recognition point of the dynamic pixel region in adjacent frame images, and determining a second dynamic parameter according to the angle between each dynamic characterization unit vector and the remaining dynamic characterization unit vectors; Step S50, determining a denoising method for continuous frame images according to the parameter correlation between the first dynamic parameter and the second dynamic parameter in a plurality of frame images; The denoising method includes screening a number of received frames and adjusting the duration of denoising processing for each dynamic pixel region in the image of the received frame; Step S60, determining the data volume corresponding to the image of the received frame after denoising, and determining the data storage redundancy of the image receiving device according to the data volume.

[0028] Specifically, determining the data amount corresponding to the image of the received frame that has completed denoising can be achieved by parsing the image data format and quantizing pixel information. This is a prior art and will not be described in detail here.

[0029] Specifically, see Figure 2 As shown, it is a step diagram of determining a dynamic pixel area according to an embodiment of the present invention. The process of determining a dynamic pixel area based on image differences between adjacent frames includes: Step S11, dividing the continuous frame images equally into a number of sub-areas; Step S12, determining the pixel value difference of each sub-region in two adjacent frames; Step S13, determining the sub-regions whose pixel value difference exceeds a preset pixel value difference threshold as dynamic pixel sub-regions, and determining the region composed of mutually adjacent dynamic pixel sub-regions as a dynamic pixel region; Specifically, the preset pixel value difference threshold can be set by technical personnel in this field according to the cloud changes, surface reflectivity changes, etc. in the actual environment. The value range of the preset pixel value difference threshold is [20, 50]. Preferably, the preset pixel value difference threshold is 30.

[0030] In implementation, the nth frame and the n+1th frame image can be divided into several sub-regions of equal size. The division method can be based on a grid division algorithm to ensure that each sub-region covers the same range of pixel coordinates. For all pixels in each sub-region, the absolute value of the difference in pixel values ​​between the two frames is calculated. This is a prior art and will not be described in detail here.

[0031] Specifically, the present invention can accurately capture the changed parts of the image by equally dividing the continuous frame images into sub-regions and then calculating the difference in pixel values ​​of each sub-region in two adjacent frames. It can be understood that the manifestation of dynamic targets in the image is often the change of pixel values. This method of calculating the difference can effectively filter out these dynamic changes from the entire image. At the same time, the adjacent dynamic pixel sub-regions are combined into dynamic pixel regions, which can completely outline the contours of the dynamic targets. By screening the dynamic pixel regions, it is avoided to perform indiscriminate processing on all pixels of the entire image, thereby realizing the screening of dynamic targets in satellite images.

[0032] Specifically, see Figure 3 As shown, it is a logic flow chart of a marking method for determining feature recognition points according to an embodiment of the present invention. The marking method for determining feature recognition points of each dynamic pixel area in an image includes: Acquire the area of ​​each dynamic pixel region, and if the area satisfies the feature distinguishing condition, determine that the marking method of the feature identification points of the dynamic pixel region is a distributed marking method; If the area does not meet the feature distinction condition, the marking method of the feature identification points of the dynamic pixel area is determined to be an overall marking method; The feature distinguishing condition is that the area exceeds the average area of ​​several dynamic pixel regions in the image.

[0033] Specifically, the present invention does not limit the specific method of obtaining the area of ​​each dynamic pixel region. Preferably, it can characterize the area size of the dynamic pixel region by counting the number of pixels in the dynamic pixel region. This is a prior art and will not be repeated here.

[0034] Specifically, the overall marking method is to mark the centroid of the area contour of the dynamic pixel area as a feature recognition point; The distribution marking method includes determining the edge of the area contour of the dynamic pixel area, and setting a plurality of feature recognition points along the edge of the area contour at a preset interval.

[0035] In implementation, the centroid of the region contour of the dynamic pixel area can be determined based on coordinate averaging and moment calculation methods, and the preset interval distance can be determined based on the region contour edge length. Preferably, the preset interval distance is the product of the region contour edge length and the interval division factor. The smaller the value of the interval division factor, the smaller the preset interval distance. Preferably, the interval division factor can be 0.1.

[0036] Specifically, the present invention selects different feature identification point marking methods according to the comparison results between the area of ​​a dynamic pixel region and the average area of ​​several dynamic pixel regions, so that the marking method is more in line with the actual characteristics of the region. It can be understood that for a smaller area, the overall marking method uses the centroid of the regional contour as the feature identification point, which can clearly reflect its overall position movement, approximate direction change and other macroscopic dynamic characteristics. For a larger area, the distributed marking method sets feature identification points along the edge of the contour, which can accurately capture the detailed dynamic changes of the region, thereby realizing the classification of dynamic targets in satellite images and avoiding the waste of resources caused by the use of complex markings in small areas.

[0037] Specifically, the process of determining the first dynamic parameter is: The position change distance is determined based on the position of the feature recognition points of each dynamic pixel area in adjacent frame images, and the average value of several position change distances is determined as the first dynamic parameter.

[0038] Specifically, see Figure 4 As shown, it is a step diagram of determining the second dynamic parameter in an embodiment of the present invention. The process of determining the second dynamic parameter includes: Step S41, taking the positions of each feature recognition point of the dynamic pixel area in the adjacent frame images as the vector starting point and the vector end point, respectively, and constructing a dynamic representation unit vector according to the vector starting point and the vector end point; Step S42, determining the dynamic characterization unit vector corresponding to each feature recognition point on the edge of the region contour of the dynamic pixel region; Step S43: determine the average value of the angles between each dynamic characterization unit vector and the remaining dynamic characterization unit vectors as the second dynamic parameter.

[0039] Specifically, the process of determining the parameter correlation between the first dynamic parameter and the second dynamic parameter includes: The first dynamic parameter and the second dynamic parameter of each frame image are acquired frame by frame in a time sequence, and the Pearson coefficient of the first dynamic parameter and the second dynamic parameter is determined as the parameter correlation.

[0040] In implementation, the Pearson coefficient may be calculated for the dimensionless values ​​of the first dynamic parameter and the second dynamic parameter.

[0041] Embodiment: If five consecutive frames of images are analyzed, from the first frame to the second frame, the feature recognition point of the dynamic pixel area moves from the position M1 (x1, y1) to the position M2 (x2, y2), and the distance d1 from the position M1 to the position M2 is calculated according to the distance formula between two points. From the second frame to the third frame, the feature recognition point moves from the position M2 (x2, y2) to the position M3 (x3, y3), and the distance d2 from the position M2 to the position M3 is calculated according to the distance formula between two points. By analogy, the distance d3 from the position M3 (x3, y3) to the position M4 (x4, y4) is obtained, and the distance d4 from the position M4 (x4, y4) to the position M5 (x5, y5) is obtained, and the first dynamic parameter D=(d1+d2+d3+d4) / 4 is calculated; If there are three feature recognition points s11 (x11, y11), s12 (x12, y12), s13 (x13, y13) on the edge of the contour of the dynamic pixel area in the first frame image, and s21 (x21, y21), s22 (x22, y22), s23 (x33, y33) in the second frame image, for feature recognition points s11 and s21, construct vector P1=(x11-x21, y11-y21), determine its unit vector |P1|, similarly, determine feature recognition points s12 and s23 22’s vector P2=(x12-x22,y12-y22),determine its unit vector |P2|,determine the vector P3=(x13-x33,y13-y33)of feature recognition point s13 and feature recognition point s23,determine its unit vector |P3|,calculate the angle between unit vector |P1| and unit vector |P2| as γ1,the angle between unit vector |P2| and unit vector |P3| as γ2,the angle between unit vector |P1| and unit vector |P3| as γ3,calculate the second dynamic parameter Q=(γ1+γ2+γ3) / 3; After the above calculations, the first dynamic parameter sequence D=[D1, D2, D3, D4, D5] and the second dynamic parameter sequence Q=[Q1, Q2, Q3, Q4, Q5] obtained in 5 consecutive frames of images, where Di and Qi are dimensionless values, i=1, 2, 3, 4, 5; By calculating the average values ​​of D1, D2, D3, D4, and D5, and the average values ​​of Q1, Q2, Q3, Q4, and Q5, the parameter correlation between the first dynamic parameter and the second dynamic parameter in five consecutive frames of images can be calculated according to the calculation formula of the Pearson coefficient, wherein the calculation formula of the Pearson coefficient is a prior art and will not be repeated here.

[0042] Specifically, the present invention determines the first dynamic parameter by calculating the average value of the position change distance of the feature identification points of each dynamic pixel area in adjacent frame images, intuitively reflects the overall movement of the dynamic pixel area, and can capture the morphological changes of the dynamic pixel area by constructing a dynamic characterization unit vector and calculating the average value of the angle between each vector. By acquiring the first dynamic parameter and the second dynamic parameter frame by frame in time sequence and calculating their Pearson coefficient as the parameter correlation, the potential relationship between the first dynamic parameter and the second dynamic parameter can be excavated. By analyzing the relationship between the first dynamic parameter and the second dynamic parameter, a model for checking the consistency of image data can be established. It can be understood that if the relationship between the first dynamic parameter and the second dynamic parameter fluctuates significantly in a certain frame or a certain area, it represents that there are obvious errors in the image data or the noise interference is too large. Furthermore, by constructing the association between the overall characteristics and individual characteristics of the dynamic target in the satellite image, more signal-representative images are screened for efficient processing, reducing the design redundancy of the receiving device, and meeting the requirements of lightweight, convenient and efficient data processing in practical applications.

[0043] Specifically, the process of screening the received frame is to determine the parameter correlation of a number of consecutive frame images in time sequence, and if the difference between the parameter correlation and a preset parameter correlation reference value meets the screening condition, the current frame corresponding to the parameter correlation is screened as the received frame; If the difference between the parameter correlation and the preset parameter correlation reference value does not meet the screening condition, the current frame is not screened; The screening condition is that the difference between the parameter correlation and a preset parameter correlation reference value is less than a preset difference threshold.

[0044] In implementation, it can be understood that the Pearson correlation coefficient, as a statistical indicator for measuring the degree of linear correlation between two variables, has a value range of [-1,1]. The closer the value is to 1, the more it indicates that there is a completely positive linear correlation between the two variables. Preferably, the preset parameter correlation reference value can be 1, and the preset difference threshold can be 0.25.

[0045] If five consecutive frames of images are analyzed, the parameter correlation between the first dynamic parameter and the second dynamic parameter in the first frame is r1=1, the parameter correlation between the first dynamic parameter and the second dynamic parameter in the first two frames is r2=0.713, the parameter correlation between the first dynamic parameter and the second dynamic parameter in the first three frames is r3=0.763, the parameter correlation between the first dynamic parameter and the second dynamic parameter in the first four frames is r4=0.755, the parameter correlation between the first dynamic parameter and the second dynamic parameter in the first five frames is r5=0.718, and the parameter correlation r2 is consistent with the preset parameter correlation parameter. The difference between the test values ​​is 1-0.713=0.287, the difference between the parameter correlation r3 and the preset parameter correlation reference value is 1-0.763=0.237, the difference between the parameter correlation r4 and the preset parameter correlation reference value is 1-0.755=0.245, and the difference between the parameter correlation r5 and the preset parameter correlation reference value is 1-0.718=0.282. Since 0.287>0.25, 0.237<0.25, 0.245<0.25, and 0.282>0.25, the 1st frame, the 3rd frame, and the 4th frame are screened as received frames.

[0046] Specifically, the present invention determines the parameter correlation of continuous frame images in time sequence, and filters the received frames according to the difference with the preset parameter correlation reference value. It can accurately avoid image frames with errors or excessive noise interference, and the filtered received frames are more representative. Furthermore, it achieves the screening of more signal-representative images for efficient processing, reduces the design redundancy of the receiving device, and meets the needs of lightweight, convenient and efficient data processing in practical applications.

[0047] Specifically, during the denoising process, the duration of adjusting the denoising process in each dynamic pixel region is negatively correlated with the difference threshold.

[0048] Specifically, the present invention adjusts the duration of the denoising process so that the denoising process can flexibly allocate resources according to actual conditions. It can be understood that the smaller the deviation between the current frame parameter correlation and the parameter correlation reference value, the more consistent the dynamic characteristics of the current frame are with expectations, and the dynamic characteristics in the image may be more significant. At this time, increasing the denoising duration can effectively remove noise from images with signal representation and more effectively retain key information in the image. Combined with the screening of received frames and the adaptive denoising duration adjustment, the optimal allocation of system resources is achieved. The screening of received frames reduces the number of frames that need to be processed, and the adjustment of the denoising duration according to the difference threshold ensures that the computing resources can be reasonably used when processing each frame. Furthermore, it is achieved that images with more signal representation are screened for efficient processing, reducing the design redundancy of the receiving device, and meeting the requirements of lightweight, convenient and efficient data processing in practical applications.

[0049] Specifically, the data storage redundancy is positively correlated with the data volume.

[0050] In implementation, the data storage redundancy can be the data volume multiplied by a preset redundancy design coefficient. The redundancy design coefficient is set by technical personnel in this field according to design requirements, and its value range is [0.05, 0.2]. Preferably, the redundancy design coefficient can be set to 0.1.

[0051] Specifically, the amount of data directly reflects the amount of image information, and the data storage redundancy is positively correlated with it, which means that the storage requirements can be accurately matched. When the data volume corresponding to the received frame image after denoising is small, based on the positive correlation, the device only needs to be configured with a small amount of storage redundancy, avoiding over-design of the storage module, reducing unnecessary hardware, and reducing the design redundancy of the receiving device, meeting the needs of lightweight, convenient and efficient data processing in practical applications.

[0052] The device provided by an embodiment of the present invention may specifically be an image receiving component or an image receiving module. The image receiving component or the image receiving module may include a connected processor and a memory, wherein the memory is used to store instructions. When the processor calls and executes the instructions, the processor may execute the device miniaturization method based on signal processing provided above.

[0053] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A device miniaturization method based on signal processing, characterized in that: include: Acquire several consecutive frames of satellite images in the scanning area in advance, and determine the dynamic pixel area based on the image difference between adjacent frames; Determine a marking method for feature identification points of each dynamic pixel region in the image according to the area of ​​each dynamic pixel region, wherein the marking method includes an overall marking method or a distributed marking method; In the overall marking mode, the first dynamic parameter is determined according to the position change of the feature recognition point of the dynamic pixel area in the adjacent frame images; In a distributed marking mode, a plurality of dynamic characterization unit vectors are constructed according to the positions of each feature recognition point of the dynamic pixel region in adjacent frame images, and a second dynamic parameter is determined according to the angle between each dynamic characterization unit vector and the remaining dynamic characterization unit vectors; Determining a denoising method for continuous frame images according to a parameter correlation between a first dynamic parameter and a second dynamic parameter in a plurality of frame images; The denoising method includes screening a number of received frames and adjusting the duration of denoising processing for each dynamic pixel region in the image of the received frame; The amount of data corresponding to the image of the received frame after denoising is determined, and the data storage redundancy of the image receiving device is determined according to the amount of data.

2. The device miniaturization method based on signal processing according to claim 1, characterized in that: The process of determining dynamic pixel areas based on image differences between adjacent frames includes: Divide the continuous frame images equally into several sub-regions; Determine the pixel value difference of each sub-region in two adjacent frames; The sub-regions whose pixel value differences exceed a preset pixel value difference threshold are determined as dynamic pixel sub-regions, and the region composed of mutually adjacent dynamic pixel sub-regions is determined as a dynamic pixel region.

3. The device miniaturization method based on signal processing according to claim 2, characterized in that: The marking methods for determining the feature recognition points of each dynamic pixel area in the image include: Acquire the area of ​​each dynamic pixel region, and if the area satisfies the feature distinguishing condition, determine that the marking method of the feature identification points of the dynamic pixel region is a distributed marking method; If the area does not meet the feature distinction condition, the marking method of the feature identification points of the dynamic pixel area is determined to be an overall marking method; The feature distinguishing condition is that the area exceeds the average area of ​​several dynamic pixel regions in the image.

4. The device miniaturization method based on signal processing according to claim 3, characterized in that: The overall marking method is to mark the centroid of the area contour of the dynamic pixel area as a feature recognition point; The distribution marking method includes determining the edge of the area contour of the dynamic pixel area, and setting a plurality of feature recognition points along the edge of the area contour at a preset interval.

5. The device miniaturization method based on signal processing according to claim 4, characterized in that: The process of determining the first dynamic parameter is: The position change distance is determined based on the position of the feature recognition points of each dynamic pixel area in adjacent frame images, and the average value of several position change distances is determined as the first dynamic parameter.

6. The device miniaturization method based on signal processing according to claim 5, characterized in that: The process of determining the second dynamic parameter includes: The positions of each feature recognition point of the dynamic pixel area in the adjacent frame images are respectively used as the vector starting point and the vector end point, and a dynamic representation unit vector is constructed according to the vector starting point and the vector end point; Determine the dynamic characterization unit vector corresponding to each feature recognition point on the edge of the area contour of the dynamic pixel area; The average value of the angles between each dynamic characterization unit vector and the remaining dynamic characterization unit vectors is determined as the second dynamic parameter.

7. The device miniaturization method based on signal processing according to claim 6, characterized in that: The process of determining the parameter correlation between the first dynamic parameter and the second dynamic parameter includes: The first dynamic parameter and the second dynamic parameter of each frame image are acquired frame by frame in time sequence, and the Pearson coefficient of the first dynamic parameter and the second dynamic parameter is determined as the parameter correlation.

8. The device miniaturization method based on signal processing according to claim 7, characterized in that: The process of screening the received frames is to determine the parameter correlation of a number of consecutive frame images in time sequence, and if the difference between the parameter correlation and a preset parameter correlation reference value meets the screening condition, the current frame corresponding to the parameter correlation is screened as the received frame; The screening condition is that the difference between the parameter correlation and a preset parameter correlation reference value is less than a preset difference threshold.

9. The device miniaturization method based on signal processing according to claim 8, characterized in that: In the denoising process, the duration of adjusting the denoising process in each dynamic pixel region is negatively correlated with the difference threshold.

10. The device miniaturization method based on signal processing according to claim 1, characterized in that: The data storage redundancy is positively correlated with the data volume.

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