Method for Miniaturizing a Device Based on Signal Processing

Abstract

The present invention relates to the technical field of image data processing, and particularly to a method for miniaturizing a device based on signal processing. The present invention determines a dynamic pixel region and a marking method for feature recognition points of each dynamic pixel region in an image. Under the overall marking method, a first dynamic parameter is determined according to the position change of the feature recognition points in adjacent frame images; under the distributed marking method, a second dynamic parameter is determined by dynamically characterizing the included angle between unit vectors; and a denoising method is determined for continuous frame images according to the parameter correlation degree between the first dynamic parameter and the second dynamic parameter. Furthermore, the present invention realizes the association of the overall features and individual features of dynamic objects in satellite images, screens images with stronger signal representativeness for efficient processing, reduces the design redundancy of receiving devices, and meets the requirements for device lightweight, convenience, and efficient data processing in practical applications.

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. Traditional image reception and processing methods often process all scanned image data comprehensively and to the same extent. This not only consumes a large amount of equipment resources, including storage space, computing power, etc., but also leads to large equipment size and increased energy consumption, which makes it difficult to meet the needs of miniaturization and lightweight equipment in practical applications. In addition, the images within the satellite scanning area contain a large amount of information, including both dynamically changing parts and relatively static parts. If these different characteristics of information 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 so as 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 based on the difference between the first pixel coordinate and the corresponding second pixel coordinate, thereby realizing denoising processing for each pixel in each frame image.

[0005] The following problems also exist in the prior art:

[0006] Existing technologies do not consider the association between overall and individual features of dynamic targets in satellite images, screening images with more signal representation for efficient processing, reducing the design redundancy of receiving equipment, and meeting the needs of lightweight, convenient and efficient data processing in practical applications. Summary of the Invention

[0007] To this end, the present invention provides a method for miniaturizing a device based on signal processing, so as to overcome the problems in the prior art that the association between the overall features and individual features of dynamic objects in satellite images cannot be constructed, images with more signal representativeness cannot be screened for efficient processing, resulting in design redundancy of the receiving device and affecting the miniaturization effect of the receiving device.

[0008] To achieve the above object, the present invention provides a method for miniaturizing a device based on signal processing, including:

[0009] Pre-acquire a plurality of consecutive frame images of a satellite within a scanning area, and determine a dynamic pixel area based on the image difference between adjacent frames;

[0010] Determine the marking method of the feature recognition points of each dynamic pixel area in the image according to the area of each dynamic pixel area, and the marking method includes an overall marking method or a distributed marking method;

[0011] Under the overall marking method, determine a first dynamic parameter according to the position change of the feature recognition points of the dynamic pixel area in adjacent frame images;

[0012] Under the distributed marking method, construct a plurality of dynamic representation unit vectors according to the positions of the feature recognition points of the dynamic pixel area in adjacent frame images, and determine a second dynamic parameter according to the included angle between each dynamic representation unit vector and the other dynamic representation unit vectors;

[0013] Determine the denoising method for the consecutive frame images according to the parameter correlation degree between the first dynamic parameter and the second dynamic parameter in a plurality of frame images;

[0014] Among them, the denoising method includes screening a plurality of received frames, and adjusting the denoising processing duration for each dynamic pixel area in the image of the received frame;

[0015] Determine the data volume corresponding to the image of the received frame after denoising is completed, and determine the data storage redundancy of the image receiving device according to the data volume.

[0016] Further, the process of determining the dynamic pixel area based on the image difference between adjacent frames includes,

[0017] Equally divide the consecutive frame images into a plurality of sub-areas;

[0018] Determine the pixel value difference between each sub-area in two adjacent frames;

[0019] Determine the sub-areas with the pixel value difference exceeding the preset pixel value difference threshold as dynamic pixel sub-areas, and determine the areas composed of adjacent dynamic pixel sub-areas as dynamic pixel areas.

[0020] Further, determining the marking method of the feature recognition points of each dynamic pixel area in the image includes,

[0021] Obtain the area of each dynamic pixel region. If the area satisfies the feature discrimination condition, determine that the marking method of the feature recognition points of the dynamic pixel region is the distribution marking method;

[0022] If the area does not satisfy the feature discrimination condition, determine that the marking method of the feature recognition points of the dynamic pixel region is the overall marking method;

[0023] Wherein, the feature discrimination condition is that the area exceeds the average area of several dynamic pixel regions in the image.

[0024] Further, the overall marking method is to mark the centroid of the region contour of the dynamic pixel region as the feature recognition point;

[0025] The distribution marking method includes determining the edge of the region contour of the dynamic pixel region and setting several feature recognition points along the edge of the region contour at a preset interval distance.

[0026] Further, the process of determining the first dynamic parameter is,

[0027] Determine the position change distance based on the positions of the feature recognition points of each dynamic pixel region in adjacent frame images, and determine the average value of several position change distances as the first dynamic parameter.

[0028] Further, the process of determining the second dynamic parameter includes,

[0029] Take the positions of the feature recognition points of the dynamic pixel region in adjacent frame images as the vector starting point and the vector ending point respectively, and construct a dynamic representation unit vector according to the vector starting point and the vector ending point;

[0030] Determine the dynamic representation unit vectors corresponding to the feature recognition points on the edge of the region contour of the dynamic pixel region;

[0031] Determine the average value of the angles between each dynamic representation unit vector and the other dynamic representation unit vectors as the second dynamic parameter.

[0032] Further, the process of determining the parameter correlation degree between the first dynamic parameter and the second dynamic parameter includes,

[0033] Obtain the first dynamic parameter and the second dynamic parameter of each frame image frame by frame according to the time sequence, and determine the Pearson coefficient of the first dynamic parameter and the second dynamic parameter as the parameter correlation degree.

[0034] Further, the process of screening the received frame is to determine the parameter correlation degree of several consecutive frame images according to the time sequence. If the difference between the parameter correlation degree and the preset parameter correlation degree reference value meets the screening condition, screen the current frame corresponding to the parameter correlation degree as the received frame;

[0035] Among them, the screening condition is that the difference between the parameter correlation degree and a preset reference value of the parameter correlation degree is less than a preset difference threshold.

[0036] Furthermore, during the denoising process, the duration of the denoising process adjusted for each dynamic pixel region is negatively correlated with the difference threshold.

[0037] Furthermore, the data storage redundancy is positively correlated with the data volume.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention determines the dynamic pixel region through the image difference between adjacent frames in a sequence of consecutive frames of images; and determines the marking method of the feature recognition points of each dynamic pixel region in the image. Under the overall marking method, the first dynamic parameter is determined according to the position change of the feature recognition points in adjacent frames of images; under the distributed marking method, the second dynamic parameter is determined by the angle between the dynamic representation unit vectors; and the denoising method for the sequence of consecutive frames of images is determined according to the parameter correlation degree between the first dynamic parameter and the second dynamic parameter. Furthermore, the association between the overall features and individual features of the dynamic objects in the satellite image is constructed, images with stronger signal representativeness are screened for efficient processing, the design redundancy of the receiving device is reduced, and the requirements for equipment lightweight, convenience, and efficient data processing in practical applications are met.

[0039] Furthermore, the present invention evenly divides the sequence of consecutive frames of images into sub-regions, and then calculates the pixel value differences between adjacent two frames for each sub-region, which can accurately capture the changed parts in the image. It can be understood that the performance of dynamic objects in the image is often the change of pixel values. This method of calculating differences can effectively screen out these dynamic changes from the entire image. At the same time, combining adjacent dynamic pixel sub-regions into a dynamic pixel region can completely outline the contour of the dynamic object. By screening the dynamic pixel region, the undifferentiated processing of all pixels in the entire image is avoided. Furthermore, the screening of the dynamic objects in the satellite image is realized.

[0040] Furthermore, the present invention selects different marking methods for feature recognition points according to the comparison result between the area of the dynamic pixel region and the average value of the areas 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 region with a small area, the overall marking method uses the centroid of the region contour as the feature recognition point, which can clearly reflect the overall position movement, approximate direction change and other macroscopic dynamic characteristics. For a region with a large area, the distributed marking method sets feature recognition points along the contour edge, which can accurately capture the detailed dynamic changes of the region. Furthermore, the classification of the dynamic objects in the satellite image is realized, and the waste of resources caused by using complex markings for small regions is avoided.

[0041] Further, the present invention determines the first dynamic parameter by calculating the average value of the position change distances of the feature recognition points in the dynamic pixel regions of adjacent frame images, which intuitively reflects the overall movement of the dynamic pixel regions. By constructing the dynamic representation unit vectors and calculating the average value of the included angles between the vectors, the morphological changes of the dynamic pixel regions can be captured. By obtaining the first dynamic parameter and the second dynamic parameter frame by frame in sequence and calculating their Pearson correlation coefficient as the parameter correlation degree, the potential relationship between the first dynamic parameter and the second dynamic parameter can be mined. By analyzing the relationship between the first dynamic parameter and the second dynamic parameter, a model for consistency checking of image data can be established. It can be understood that if there are obvious fluctuations in the relationship between the first dynamic parameter and the second dynamic parameter in a certain frame or a certain region, it indicates that there are obvious errors or excessive noise interference in the image data. Furthermore, by constructing the association between the overall features and individual features of the dynamic objects in the satellite image, images with stronger signal representativeness are selected for efficient processing, reducing the design redundancy of the receiving device and meeting the requirements of equipment lightweight, convenience, and efficient data processing in practical applications.

[0042] Further, the present invention determines the parameter correlation degree of consecutive frame images in sequence and screens the received frames based on the difference from the preset parameter correlation degree reference value, which can accurately avoid image frames with errors or excessive noise interference. The selected received frames are more representative. Furthermore, it realizes the selection of images with stronger signal representativeness for efficient processing, reduces the design redundancy of the receiving device, and meets the requirements of equipment lightweight, convenience, and efficient data processing in practical applications.

[0043] Further, the present invention adjusts the duration of the denoising process, enabling the denoising process to flexibly allocate resources according to the actual situation. It can be understood that the smaller the deviation degree of the current frame parameter correlation degree from the parameter correlation degree reference value, the more consistent the dynamic features of the current frame are with the expectation, and the more significant the dynamic features in the image may be. At this time, increasing the denoising duration can effectively remove noise from images with signal representativeness and more effectively retain the key information in the images. Combining the screening of received frames and the adaptive adjustment of the denoising duration realizes the optimal allocation of system resources. Screening the received frames reduces the number of frames to be processed, and adjusting the denoising duration according to the difference threshold ensures that the computing resources can be reasonably utilized when processing each frame. Furthermore, it realizes the selection of images with stronger signal representativeness for efficient processing, reduces the design redundancy of the receiving device, and meets the requirements of equipment lightweight, convenience, and efficient data processing in practical applications. Description of the Drawings

[0044] Figure 1 It is a step diagram of the method for miniaturizing the device based on signal processing according to the embodiment of the present invention;

[0045] Figure 2 It is a step diagram for determining the dynamic pixel region in an embodiment of the present invention;

[0046] Figure 3 It is a logic flowchart for determining the marking method of feature recognition points in an embodiment of the present invention;

[0047] Figure 4 It is a step diagram for determining the second dynamic parameter in an embodiment of the present invention. Detailed implementation manners

[0048] In order to make the objectives and advantages of the present invention more clear, the present invention will be 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.

[0049] The preferred implementation manners of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0050] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for 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, and therefore should not be construed as a limitation of the present invention.

[0051] Please refer to Figure 1 As shown, it is a step diagram of the method for miniaturizing a device based on signal processing in an embodiment of the present invention. The method for miniaturizing a device based on signal processing of the present invention includes:

[0052] Step S10: Pre-acquire a plurality of consecutive frame images of a satellite in a scanning area, and determine the dynamic pixel region based on the image difference between adjacent frames;

[0053] Step S20: Determine the marking method of the feature recognition points of each dynamic pixel region in the image according to the area of each dynamic pixel region. The marking method includes an overall marking method or a distributed marking method;

[0054] Step S30: Under the overall marking method, determine the first dynamic parameter according to the position change of the feature recognition points of the dynamic pixel region in adjacent frame images;

[0055] Step S40: Under the distributed marking method, construct a plurality of dynamic characterization unit vectors according to the positions of the feature recognition points of the dynamic pixel region in adjacent frame images, and determine the second dynamic parameter according to the angles between each dynamic characterization unit vector and the other dynamic characterization unit vectors;

[0056] Step S50: Determine the denoising method for consecutive frame images according to the parameter correlation degree between the first dynamic parameter and the second dynamic parameter in several frames of images;

[0057] Among them, the denoising method includes screening several received frames and adjusting the denoising processing duration for each dynamic pixel region in the received frame images;

[0058] Step S60: Determine the data volume corresponding to the image of the received frame after denoising, and determine the data storage redundancy of the image receiving device according to the data volume.

[0059] Specifically, determining the data volume corresponding to the image of the received frame after denoising can be achieved by parsing the image data format and quantifying pixel information. This is prior art and will not be elaborated here.

[0060] Specifically, please refer to Figure 2 As shown, it is the step diagram for determining the dynamic pixel region in the embodiment of the present invention. The process of determining the dynamic pixel region based on the image difference between adjacent frames includes

[0061] Step S11: Evenly divide consecutive frame images into several sub-regions;

[0062] Step S12: Determine the pixel value difference between adjacent two frames for each sub-region;

[0063] Step S13: Determine the sub-regions with the pixel value difference exceeding the preset pixel value difference threshold as dynamic pixel sub-regions, and determine the regions composed of adjacent dynamic pixel sub-regions as dynamic pixel regions;

[0064] Specifically, the preset pixel value difference threshold can be set by those skilled in the art according to the cloud changes and surface reflectivity changes 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.

[0065] In implementation, the nth frame and the (n + 1)th frame images can be divided into several sub-regions of equal size. The division method can be based on the grid division algorithm to ensure that each sub-region covers the same range of pixel coordinates. For all pixels in each sub-region, calculate the absolute value of the pixel value difference between the two frames. This is prior art and will not be elaborated here.

[0066] Specifically, the present invention evenly divides consecutive frame images into sub-regions, and then calculates the pixel value differences of each sub-region between two adjacent frames, which can accurately capture the changed parts in the images. It can be understood that the performance of dynamic targets in images is often the change of pixel values. This method of calculating differences can effectively screen out these dynamic changes from the entire image. At the same time, combining adjacent dynamic pixel sub-regions into dynamic pixel regions can completely outline the contours of dynamic targets. By screening dynamic pixel regions, it avoids non-discriminatory processing of all pixels in the entire image. Furthermore, the screening of dynamic target objects in satellite images is achieved.

[0067] Specifically, please refer to Figure 3 As shown, it is a logic flow chart of the marking method for determining feature recognition points in an embodiment of the present invention. The marking method for determining feature recognition points of each dynamic pixel region in the image includes

[0068] Obtain the area of each dynamic pixel region. If the area meets the feature discrimination condition, determine that the marking method for the feature recognition point of the dynamic pixel region is the distributed marking method;

[0069] If the area does not meet the feature discrimination condition, determine that the marking method for the feature recognition point of the dynamic pixel region is the overall marking method;

[0070] Among them, the feature discrimination condition is that the area exceeds the average area of several dynamic pixel regions in the image.

[0071] Specifically, the present invention does not limit the specific method for 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 pixel points in the dynamic pixel region. This is prior art and will not be elaborated here.

[0072] Specifically, the overall marking method is to mark the centroid of the region contour of the dynamic pixel region as the feature recognition point;

[0073] The distributed marking method includes determining the edge of the region contour of the dynamic pixel region and setting several feature recognition points along the edge of the region contour at a preset interval distance.

[0074] In implementation, methods such as coordinate averaging and moment calculation can be used to determine the centroid of the region contour of the dynamic pixel region, and the preset interval distance can be determined according to the length of the region contour edge. Preferably, the preset interval distance is the product of the length of the region contour edge 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.

[0075] Specifically, according to the comparison result between the area of the dynamic pixel region and the average value of the areas of several dynamic pixel regions, the present invention selects different marking methods for feature recognition points, making the marking method more in line with the actual features of the region. It can be understood that for a region with a small area, the overall marking method uses the centroid of the region contour as the feature recognition point, which can clearly reflect the overall position movement, approximate direction change and other macroscopic dynamic features. For a region with a large area, the distributed marking method sets feature recognition points along the contour edge, which can accurately capture the detailed dynamic changes of the region. Furthermore, the classification of dynamic objects in satellite images is realized, and the waste of resources caused by using complex markings for small regions is avoided.

[0076] Specifically, the process of determining the first dynamic parameter is as follows:

[0077] Based on the positions of the feature recognition points of each dynamic pixel region in adjacent frame images, the position change distance is determined, and the average value of several position change distances is determined as the first dynamic parameter.

[0078] Specifically, please refer to Figure 4 As shown, it is a step diagram for determining the second dynamic parameter in an embodiment of the present invention. The process of determining the second dynamic parameter includes:

[0079] Step S41: Respectively use the positions of the feature recognition points of the dynamic pixel region in adjacent frame images as the starting point and the ending point of the vector, and construct a dynamic representation unit vector according to the starting point and the ending point of the vector;

[0080] Step S42: Determine the dynamic representation unit vectors corresponding to the feature recognition points on the region contour edge of the dynamic pixel region;

[0081] Step S43: Determine the average value of the included angles between each dynamic representation unit vector and the rest of the dynamic representation unit vectors as the second dynamic parameter.

[0082] Specifically, the process of determining the parameter correlation degree between the first dynamic parameter and the second dynamic parameter includes:

[0083] Acquire the first dynamic parameter and the second dynamic parameter of each frame image frame by frame in time sequence, and determine the Pearson coefficient of the first dynamic parameter and the second dynamic parameter as the parameter correlation degree.

[0084] In implementation, the Pearson coefficient can be calculated for the dimensionless numerical values of the first dynamic parameter and the second dynamic parameter.

[0085] Embodiment: If five consecutive frames of images are analyzed, from the first frame to the second frame, the feature recognition points in the dynamic pixel region move from position M1(x1, y1) to position M2(x2, y2). The distance d1 from position M1 to position M2 is calculated according to the distance formula between two points. From the second frame to the third frame, the feature recognition points move from position M2(x2, y2) to position M3(x3, y3), and the distance d2 from position M2 to position M3 is calculated according to the distance formula between two points. And so on, the distance d3 from the feature recognition points moving from position M3(x3, y3) to position M4(x4, y4) is obtained, and the distance d4 from the feature recognition points moving from position M4(x4, y4) to position M5(x5, y5) is obtained. The first dynamic parameter D = (d1 + d2 + d3 + d4) / 4 is calculated;

[0086] If there are three feature recognition points s11(x11, y11), s12(x12, y12), s13(x13, y13) on the contour edge of the dynamic pixel region in the first frame image, and s21(x21, y21), s22(x22, y22), s23(x33, y33) in the second frame image. For the feature recognition point s11 and the feature recognition point s21, a vector P1 = (x11 - x21, y11 - y21) is constructed, and its unit vector |P1| is determined. Similarly, the vector P2 = (x12 - x22, y12 - y22) of the feature recognition point s12 and the feature recognition point s22 is determined, and its unit vector |P2| is determined. The vector P3 = (x13 - x33, y13 - y33) of the feature recognition point s13 and the feature recognition point s23 is determined, and its unit vector |P3| is determined. The included angle between the unit vector |P1| and the unit vector |P2| is calculated as γ1, the included angle between the unit vector |P2| and the unit vector |P3| is calculated as γ2, and the included angle between the unit vector |P1| and the unit vector |P3| is calculated as γ3. The second dynamic parameter Q = (γ1 + γ2 + γ3) / 3 is calculated;

[0087] 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] are obtained in five consecutive frames of images, where Di and Qi are the values after removing the dimension, and i = 1, 2, 3, 4, 5;

[0088] By calculating the average values of D1, D2, D3, D4, D5 and the average values of Q1, Q2, Q3, Q4, Q5, and then according to the calculation formula of the Pearson coefficient, the parameter correlation degree between the first dynamic parameter and the second dynamic parameter in five consecutive frames of images can be calculated. The calculation formula of the Pearson coefficient is prior art and will not be elaborated here.

[0089] Specifically, the present invention determines the first dynamic parameter by calculating the average value of the position change distances of the feature recognition points in the dynamic pixel regions of adjacent frame images, which intuitively reflects the overall movement of the dynamic pixel regions. By constructing dynamic representation unit vectors and calculating the average value of the included angles between the vectors, the morphological changes of the dynamic pixel regions can be captured. By sequentially obtaining the first dynamic parameter and the second dynamic parameter frame by frame according to the time sequence and calculating their Pearson correlation coefficient as the parameter correlation degree, the potential relationship between the first dynamic parameter and the second dynamic parameter can be mined. By analyzing the relationship between the first dynamic parameter and the second dynamic parameter, a model for consistency checking of image data can be established. It can be understood that if there are obvious fluctuations in the relationship between the first dynamic parameter and the second dynamic parameter in a certain frame or a certain region, it indicates that there are obvious errors in the image data or the noise interference is too large. Furthermore, by constructing the association between the overall features and individual features of the dynamic targets in the satellite images, images with stronger signal representativeness are screened for efficient processing, reducing the design redundancy of the receiving device and meeting the requirements for device lightweight, convenience, and efficient data processing in practical applications.

[0090] Specifically, the process of screening the received frames is to determine the parameter correlation degree of several consecutive frame images according to the time sequence. If the difference between the parameter correlation degree and the preset parameter correlation degree reference value meets the screening condition, the current frame corresponding to the parameter correlation degree is screened as the received frame;

[0091] If the difference between the parameter correlation degree and the preset parameter correlation degree reference value does not meet the screening condition, the current frame is not screened;

[0092] Wherein, the screening condition is that the difference between the parameter correlation degree and the preset parameter correlation degree reference value is less than the preset difference threshold.

[0093] In implementation, it can be understood that the Pearson correlation coefficient, as a statistical index for measuring the linear correlation degree between two variables, has a value range of [-1, 1]. The closer its value is to 1, the more it indicates a complete positive linear correlation relationship between the two variables. Preferably, the preset parameter correlation degree reference value can be 1, and the preset difference threshold can be 0.25.

[0094] If five consecutive frames of images are analyzed, the correlation degree r1 of the first dynamic parameter and the second dynamic parameter in the first frame of image is 1, the correlation degree r2 of the first dynamic parameter and the second dynamic parameter in the first two frames of images is 0.713, the correlation degree r3 of the first dynamic parameter and the second dynamic parameter in the first three frames of images is 0.763, the correlation degree r4 of the first dynamic parameter and the second dynamic parameter in the first four frames of images is 0.755, and the correlation degree r5 of the first dynamic parameter and the second dynamic parameter in the first five frames of images is 0.718. The difference between the correlation degree r2 and the preset reference value of the correlation degree of parameters is 1 - 0.713 = 0.287, the difference between the correlation degree r3 and the preset reference value of the correlation degree of parameters is 1 - 0.763 = 0.237, the difference between the correlation degree r4 and the preset reference value of the correlation degree of parameters is 1 - 0.755 = 0.245, and the difference between the correlation degree r5 and the preset reference value of the correlation degree of parameters is 1 - 0.718 = 0.282. Since 0.287 > 0.25, 0.237 < 0.25, 0.245 < 0.25, 0.282 > 0.25, the first frame, the third frame, and the fourth frame are selected as received frames.

[0095] Specifically, the present invention determines the correlation degree of parameters of consecutive frames of images according to the time sequence, and screens the received frames based on the difference from the preset reference value of the correlation degree of parameters, which can accurately avoid image frames with errors or excessive noise interference. The selected received frames are more representative. Furthermore, it realizes screening images with more signal representativeness for efficient processing, reduces the design redundancy of the receiving device, and meets the requirements of equipment lightweight, convenience, and efficient data processing in practical applications.

[0096] Specifically, during the denoising process, the duration of the denoising process for each dynamic pixel region is negatively correlated with the difference threshold.

[0097] Specifically, the present invention adjusts the duration of the denoising process, enabling the denoising process to flexibly allocate resources according to the actual situation. It can be understood that the smaller the deviation degree of the correlation degree of parameters of the current frame from the reference value of the correlation degree of parameters, the more consistent the dynamic characteristics of the current frame are with the expectation, and the more significant the dynamic characteristics in the image may be. At this time, increasing the denoising duration can effectively remove noise from images with signal representativeness and more effectively retain the key information in the images. Combining the screening of received frames and the adaptive adjustment of the denoising duration realizes the optimal allocation of system resources. Screening received frames reduces the number of frames to be processed, and adjusting the denoising duration according to the difference threshold ensures that computing resources can be reasonably utilized when processing each frame. Furthermore, it realizes screening images with more signal representativeness for efficient processing, reduces the design redundancy of the receiving device, and meets the requirements of equipment lightweight, convenience, and efficient data processing in practical applications.

[0098] Specifically, the data storage redundancy is positively correlated with the data volume.

[0099] 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 those skilled in the art according to design requirements, and its value range is [0.05, 0.2]. Preferably, the redundancy design coefficient can be set to 0.1.

[0100] Specifically, the data volume directly reflects the amount of image information. The positive correlation between the data storage redundancy and it means that the storage requirements can be accurately matched. When the data volume corresponding to the received frame image after denoising is small, according to the positive correlation relationship, the device only needs to configure a small amount of storage redundancy, avoiding over-design of the storage module, reducing unnecessary hardware, reducing the design redundancy of the receiving device, and meeting the requirements for lightweight, convenient, and efficient data processing of the device in practical applications.

[0101] The device provided by the embodiment of the present invention can specifically be an image receiving component or an image receiving module. The image receiving component or the image receiving module can include a connected processor and a memory. Among them, the memory is used to store instructions. When the processor calls and executes the instructions, the processor can execute the above-provided method for miniaturizing the device based on signal processing.

[0102] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the 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.

[0103] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, 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 method for miniaturizing a device based on signal processing, characterized in that, Including: Pre-acquire a number of consecutive frame images of a satellite within a scanning area, and determine dynamic pixel regions based on the image differences between adjacent frames; Determine the marking method of the feature recognition points of each dynamic pixel region in the image according to the area of each dynamic pixel region, and the marking method includes an overall marking method or a distributed marking method; Determining the marking method includes obtaining the area of each dynamic pixel region. If the area meets the feature discrimination condition, determine that the marking method of the feature recognition points of the dynamic pixel region is the distributed marking method; If the area does not meet the feature discrimination condition, determine that the marking method of the feature recognition points of the dynamic pixel region is the overall marking method; Wherein, the feature discrimination condition is that the area exceeds the average area of a number of dynamic pixel regions in the image; Under the overall marking method, determine the first dynamic parameter according to the position change of the feature recognition points of the dynamic pixel regions in adjacent frame images; Under the distributed marking method, construct a number of dynamic characterization unit vectors according to the positions of the feature recognition points of the dynamic pixel region in adjacent frame images, and determine the second dynamic parameter according to the angles between each dynamic characterization unit vector and the remaining dynamic characterization unit vectors; Determine the denoising method for the continuous frame images according to the parameter correlation degree between the first dynamic parameter and the second dynamic parameter in a number of frame images; Wherein, sequentially obtain the first dynamic parameter and the second dynamic parameter of each frame image according to the time sequence, and determine the Pearson coefficient of the first dynamic parameter and the second dynamic parameter as the parameter correlation degree; The denoising method includes screening a number of received frames and adjusting the denoising processing duration for each dynamic pixel region in the image of the received frame; Determine the data volume corresponding to the image of the received frame after denoising is completed, and determine the data storage redundancy of the image receiving device according to the data volume.

2. The method for miniaturizing a device based on signal processing according to claim 1, wherein The process of determining the dynamic pixel region based on the image difference between adjacent frames includes, Equally divide the continuous frame images into a number of sub-regions; Determine the pixel value difference of each sub-region between adjacent two frames; Determine the sub-regions with pixel value differences exceeding the preset pixel value difference threshold as dynamic pixel sub-regions, and determine the regions composed of adjacent dynamic pixel sub-regions as dynamic pixel regions.

3. The method for miniaturizing a signal processing-based device according to claim 1, wherein The overall marking method is to mark the centroid of the region contour of the dynamic pixel region as the feature recognition point; The distributed marking method includes determining the region contour edge of the dynamic pixel region and setting a number of feature recognition points along the region contour edge at a preset interval distance.

4. The method for miniaturizing a signal processing-based device according to claim 3, wherein The process of determining the first dynamic parameter is, Determine the position change distance based on the positions of the feature recognition points of each dynamic pixel region in adjacent frame images, and determine the average value of a number of position change distances as the first dynamic parameter.

5. The method for miniaturizing a signal processing-based device according to claim 4, wherein The process of determining the second dynamic parameter includes, Respectively use the positions of the feature recognition points of the dynamic pixel region in adjacent frame images as the vector starting point and the vector ending point, and construct a dynamic characterization unit vector according to the vector starting point and the vector ending point; Determine the dynamic characterization unit vectors corresponding to the feature recognition points on the region contour edge of the dynamic pixel region; 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.

6. The method for miniaturizing a signal processing-based device according to claim 1, wherein The process of screening received frames is to determine the parameter correlation degree of several consecutive frame images in sequence. If the difference between the parameter correlation degree and the preset reference value of the parameter correlation degree meets the screening condition, the current frame corresponding to the parameter correlation degree is screened as the received frame; Among them, the screening condition is that the difference between the parameter correlation degree and the preset reference value of the parameter correlation degree is less than the preset difference threshold.

7. The method for miniaturizing a signal processing-based device according to claim 6, characterized in that During the denoising process, the denoising processing duration of each dynamic pixel region is negatively correlated with the preset difference threshold.

8. The method for miniaturizing a signal processing-based device according to claim 1, wherein The data storage redundancy is positively correlated with the data volume.

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