Airborne image processing method based on long-distance image transmission

Abstract

The invention discloses an airborne image processing method based on long-distance image transmission, and relates to the technical field of image processing, and the method comprises the following steps: S1, employing an airborne camera to collect an original image; s2, acquiring image transmission link state parameters for transmitting the original image, performing image airborne terminal preprocessing according to the image transmission link state parameters to obtain a newest image, and transmitting the newest image; and S3, carrying out dynamic enhancement on the received latest image by utilizing a ground receiving end. According to the method, block processing is adopted for heterogeneous characteristics of remote image transmission local interference, multipath fading and bandwidth fluctuation; processing is completed in combination with physical parameters of the image transmission link and content characteristics of the image, so that image processing always fits the actual interference state of the link.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and more specifically to an airborne image processing method based on long-distance image transmission. Background Technology

[0002] With the rapid development of UAVs and airborne reconnaissance technology, the demand for long-distance image transmission is becoming increasingly urgent. However, the transmission from airborne equipment to the ground receiving end is susceptible to various factors: (1) at the channel level: there are problems such as multipath fading, electromagnetic interference (such as co-channel interference from other wireless devices), and unstable channel gain; (2) at the transmission level: long distances lead to significant signal power attenuation, which can easily result in transmission errors, image block loss, or distortion. These problems directly lead to defects such as dense noise, blurred details, and texture distortion in the transmitted images, which seriously affect the subsequent image analysis and application (such as target recognition and scene assessment).

[0003] In addition, existing airborne image processing has poor adaptability: existing airborne image processing mostly uses denoising algorithms with fixed thresholds, without taking into account the real-time noise type of the image transmission link, resulting in over-denoising (loss of details) or under-denoising (residual noise). Summary of the Invention

[0004] To address the above problems, this invention proposes an airborne image processing method based on long-distance image transmission.

[0005] The technical solution of this invention is: an airborne image processing method based on long-distance image transmission, comprising the following steps:

[0006] S1. Acquire raw images using the airborne camera;

[0007] S2. Collect the image transmission link status parameters for transmission of the original image, and perform image preprocessing on the airborne terminal according to the image transmission link status parameters to obtain the latest image and transmit it.

[0008] S3. Utilize the ground receiver to dynamically enhance the latest received images.

[0009] Furthermore, S2 includes the following sub-steps:

[0010] S21. Divide the original image into blocks to obtain several image blocks;

[0011] S22. Collect the image transmission link status parameters for transmitting the original image, and calculate the signal transmission interference value of the original image based on several image blocks.

[0012] S23. Obtain the image transmission link status parameters of historical images, calculate the signal transmission interference value of each historical image, and set the average signal transmission interference value of all historical images as the transmission threshold.

[0013] S24. When the signal transmission interference value is greater than the transmission threshold, the image block is subjected to Gaussian kernel function weighted fusion processing; when the signal transmission interference value is less than or equal to the transmission threshold, the image block is subjected to mean filtering.

[0014] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, when the total image data volume is extremely large, the transmission latency is high (e.g., >200ms under a 20km link), which cannot meet the low latency requirements for real-time drone control and real-time security early warning. When the link bandwidth fluctuates (e.g., drops sharply from 10Mbps to 2Mbps), the total image compression rate needs to be significantly adjusted, which can easily lead to over-compression distortion or bandwidth redundancy waste. Block transmission is a refined optimization of total image transmission. Gaussian kernel weighted fusion is used for blocks with severe interference (preserving details while suppressing noise), and mean filtering is used for blocks with slight interference (efficient noise reduction).

[0015] Furthermore, S21 includes the following sub-steps:

[0016] S221. Acquire the image transmission link status parameters for transmission; wherein, the image transmission link status parameters include the image block operating frequency and modulation phase;

[0017] S222. Based on the image block image transmission link status parameters, the transmission energy coefficient of the image block during the single block transmission duration is determined using a cosine function.

[0018] S223. Input the negative of the ratio between the minimum transmission energy coefficient and the maximum transmission energy coefficient into the exponential function as the interference indicator weight of the original image.

[0019] S224. Calculate the signal transmission interference value of the original image based on the interference indication weight of the original image.

[0020] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, the image block operating frequency and modulation phase are the core physical parameters of the link. Long-distance image transmission often employs frequency hopping technology (dynamic frequency switching) and phase modulation (control signal waveform), and these parameters directly determine the signal transmission characteristics in the channel. The transmission energy coefficient is essentially the aggregation of time-frequency domain energy. The negative of the minimum / maximum transmission energy coefficient is input into an exponential function to obtain the interference indication weight. The greater the energy fluctuation (the smaller the minimum / maximum ratio), the more unstable the energy distribution in the time-frequency domain of the link (e.g., frequency conflicts causing a sudden drop in energy in some blocks), and the more severe the interference.

[0021] Furthermore, in S222, the transmission energy coefficient The expression is:

[0022] ;

[0023] in, Indicates the first The operating frequency of each image block Indicates the first The modulation phase of each image block, This indicates the transmission power of the image transmission link. This indicates the number of image patches in the original image. Indicates the start time of image block transmission.

[0024] The beneficial effects of the above-mentioned further solutions are as follows: In the present invention, in anti-interference image transmission scenarios, in order to avoid dynamic interference (such as sudden electromagnetic signals and conflicts with devices on the same frequency), a set of available frequency points is usually preset (for example, dividing the 5.8GHz band into 8 clean sub-frequency points). Before transmission, each image block will be transmitted by selecting the frequency point with the least interference in the set through link interference detection (such as real-time monitoring of the interference power of each frequency point). A single image block uses only one frequency point in one transmission process and will not occupy multiple frequency points at the same time.

[0025] Furthermore, in S224, the signal transmission interference value of the original image The expression is:

[0026] ;

[0027] in, The interference indicator weights represent the original image. This indicates the transmission power of the interference signal. This represents the white Gaussian noise at the ground receiver. This represents the channel gain from the airborne terminal to the ground receiver. This indicates the transmission power of the image transmission link. This represents the channel gain of the interference signal.

[0028] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, the transmit power refers to the signal power output by the airborne transmitter (such as a UAV image transmission radio), which directly determines the signal transmission distance and anti-interference capability; when an image uses a uniform transmit power, each image block can still have its own operating frequency and modulation phase. Traditional SINR formulas only consider interference power, neglecting the dynamic fluctuations of link energy. For example, two regions may have the same interference power, but due to different levels of link energy fluctuation, the actual perceived interference differs significantly. This formula can accurately distinguish this difference, making subsequent decisions regarding the comparison of interference values ​​with thresholds more accurate.

[0029] Furthermore, in S24, the Gaussian kernel weighted fusion processing of the image patches includes the following sub-steps:

[0030] S241. Acquire the gray-level co-occurrence matrix of the original image and the local gray-level co-occurrence matrix of each image block;

[0031] S242. Determine the entropy difference of the image blocks based on the gray-level co-occurrence matrix of each local gray-level matrix and the gray-level co-occurrence matrix of the original image;

[0032] S243. Perform clustering on all entropy differences to determine the clusters corresponding to the image patches;

[0033] S244. The ratio between the entropy difference of the image patch and the center value of the cluster to which the image patch belongs is used as the adjustment weight of the Gaussian kernel;

[0034] S245. The variance of the Gaussian kernel is optimized by adjusting the weights of the Gaussian kernel, and the optimized Gaussian kernel is used to complete the weighted fusion processing of the Gaussian kernel function on the image patch.

[0035] The beneficial effect of the above further scheme is that, in this invention, entropy measures the randomness of gray-level co-occurrence in GLCM. The ratio of the entropy difference of an image block to the value of its cluster center is used as the Gaussian kernel adjustment weight. The cluster center value is the average entropy difference of that type of block. The larger the ratio of the entropy difference to the cluster center, the more extreme the deviation of the block within the cluster, requiring a stronger Gaussian kernel fusion (e.g., increasing the variance to expand the fusion range). The Gaussian kernel variance is optimized by adjusting the weights to complete the weighted fusion of image blocks.

[0036] Furthermore, in S242, the absolute value of the difference between the entropy of each local gray-level co-occurrence matrix and the entropy of the gray-level co-occurrence matrix of the original image is calculated as the entropy difference of the image patch.

[0037] Furthermore, in S3, adaptive gamma correction is used to dynamically enhance the received latest image.

[0038] The beneficial effects of this invention are:

[0039] (1) In view of the heterogeneous characteristics of local interference, multipath fading and bandwidth fluctuation in long-distance image transmission, the present invention adopts block processing; the processing is completed by combining the physical parameters of the image transmission link and the content features of the image, so that the image processing always fits the actual interference state of the link.

[0040] (2) This invention is applicable to long-distance image transmission scenarios of airborne platforms such as UAVs, helicopters and fixed-wing aircraft, and realizes collaborative optimization of airborne lightweight preprocessing + link transmission + ground fine post-processing to improve image quality and real-time performance under long-distance image transmission. Attached Figure Description

[0041] Figure 1 This is a flowchart of an airborne image processing method based on long-distance image transmission. Detailed Implementation

[0042] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0043] like Figure 1 As shown, this invention provides an airborne image processing method based on long-distance image transmission, comprising the following steps:

[0044] S1. Acquire raw images using the airborne camera;

[0045] S2. Collect the image transmission link status parameters for transmission of the original image, and perform image preprocessing on the airborne terminal according to the image transmission link status parameters to obtain the latest image and transmit it.

[0046] S3. Utilize the ground receiver to dynamically enhance the latest received images.

[0047] In this embodiment of the invention, S2 includes the following sub-steps:

[0048] S21. Divide the original image into blocks to obtain several image blocks;

[0049] S22. Collect the image transmission link status parameters for transmitting the original image, and calculate the signal transmission interference value of the original image based on several image blocks.

[0050] S23. Obtain the image transmission link status parameters of historical images, calculate the signal transmission interference value of each historical image, and set the average signal transmission interference value of all historical images as the transmission threshold.

[0051] S24. When the signal transmission interference value is greater than the transmission threshold, the image block is subjected to Gaussian kernel function weighted fusion processing; when the signal transmission interference value is less than or equal to the transmission threshold, the image block is subjected to mean filtering.

[0052] In this invention, when the total image data volume is extremely large, the transmission latency is high (e.g., >200ms under a 20km link), which cannot meet the low latency requirements for real-time drone control and real-time security early warning. When the link bandwidth fluctuates (e.g., drops sharply from 10Mbps to 2Mbps), the total image compression rate needs to be significantly adjusted, which can easily lead to over-compression distortion or bandwidth redundancy waste. Block transmission is a refined optimization of total image transmission. Gaussian kernel weighted fusion is used for blocks with severe interference (preserving details while suppressing noise), while mean filtering is used for blocks with slight interference (efficient noise reduction).

[0053] In this embodiment of the invention, S21 includes the following sub-steps:

[0054] S221. Acquire the image transmission link status parameters for transmission; wherein, the image transmission link status parameters include the image block operating frequency and modulation phase;

[0055] S222. Based on the image block image transmission link status parameters, the transmission energy coefficient of the image block during the single block transmission duration is determined using a cosine function.

[0056] S223. Input the negative of the ratio between the minimum transmission energy coefficient and the maximum transmission energy coefficient into the exponential function as the interference indicator weight of the original image.

[0057] S224. Calculate the signal transmission interference value of the original image based on the interference indication weight of the original image.

[0058] In this invention, the image block operating frequency and modulation phase are the core physical parameters of the link. Long-distance image transmission often employs frequency hopping technology (dynamic frequency switching) and phase modulation (control signal waveform), and these parameters directly determine the signal transmission characteristics in the channel. The transmission energy coefficient is essentially an aggregation of time-frequency domain energy. The inverse of the minimum / maximum transmission energy coefficient is input into an exponential function to obtain the interference indication weight. The greater the energy fluctuation (the smaller the minimum / maximum ratio), the more unstable the energy distribution in the time-frequency domain of the link (e.g., frequency conflicts causing a sudden drop in energy in some blocks), and the more severe the interference.

[0059] In this embodiment of the invention, in S222, the transmission energy coefficient The expression is:

[0060] ;

[0061] in, Indicates the first The operating frequency of each image block Indicates the first The modulation phase of each image block, This indicates the transmission power of the image transmission link. This indicates the number of image patches in the original image. Indicates the start time of image block transmission.

[0062] In this invention, in anti-interference image transmission scenarios, to avoid dynamic interference (such as sudden electromagnetic signals and conflicts with devices on the same frequency), a set of available frequency points is usually preset (for example, dividing the 5.8GHz band into 8 clean sub-frequency points). Before transmission, each image block will be transmitted using the frequency point with the least interference in the set through link interference detection (such as real-time monitoring of the interference power of each frequency point). A single image block uses only one frequency point in one transmission process and will not occupy multiple frequency points simultaneously.

[0063] In this embodiment of the invention, in S224, the signal transmission interference value of the original image The expression is:

[0064] ;

[0065] in, The interference indicator weights represent the original image. This indicates the transmission power of the interference signal. This represents the white Gaussian noise at the ground receiver. This represents the channel gain from the airborne terminal to the ground receiver. This indicates the transmission power of the image transmission link. This represents the channel gain of the interference signal.

[0066] In this invention, transmit power refers to the signal power output by the airborne transmitter (such as a UAV image transmission radio), which directly determines the signal transmission distance and anti-interference capability. When an image uses a uniform transmit power, each image block can still have its own operating frequency and modulation phase. Traditional SINR formulas only consider interference power and ignore the dynamic fluctuations of link energy. For example, two regions may have the same interference power, but due to different levels of link energy fluctuation, the actual perceived interference will be significantly different. This formula can accurately distinguish such differences, making subsequent decisions on comparing interference values ​​with thresholds more accurate.

[0067] In this embodiment of the invention, step S24, which involves performing Gaussian kernel weighted fusion processing on the image blocks, includes the following sub-steps:

[0068] S241. Acquire the gray-level co-occurrence matrix of the original image and the local gray-level co-occurrence matrix of each image block;

[0069] S242. Determine the entropy difference of the image blocks based on the gray-level co-occurrence matrix of each local gray-level matrix and the gray-level co-occurrence matrix of the original image;

[0070] S243. Perform clustering on all entropy differences to determine the clusters corresponding to the image patches;

[0071] S244. The ratio between the entropy difference of the image patch and the center value of the cluster to which the image patch belongs is used as the adjustment weight of the Gaussian kernel;

[0072] S245. The variance of the Gaussian kernel is optimized by adjusting the weights of the Gaussian kernel, and the optimized Gaussian kernel is used to complete the weighted fusion processing of the Gaussian kernel function on the image patch.

[0073] In this invention, entropy in GLCM measures the randomness of gray-level co-occurrence. The ratio of the entropy difference of an image patch to the cluster center value is used as the Gaussian kernel adjustment weight. The cluster center value is the average entropy difference of that class of patches. The larger the ratio of the entropy difference to the cluster center, the more extreme the deviation of the patch within the cluster, requiring a stronger Gaussian kernel fusion (e.g., increasing the variance to expand the fusion range). The Gaussian kernel variance is optimized using the adjusted weights to complete the weighted fusion of image patches.

[0074] In this embodiment of the invention, in S242, the absolute value of the difference between the entropy of each local gray-level co-occurrence matrix and the entropy of the gray-level co-occurrence matrix of the original image is calculated as the entropy difference of the image block.

[0075] In this embodiment of the invention, in S3, adaptive gamma correction is used to dynamically enhance the received latest image.

[0076] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. An airborne image processing method based on long-distance image transmission, characterized in that, Includes the following steps: S1. Acquire raw images using the airborne camera; S2. Collect the image transmission link status parameters for transmission of the original image, and perform image preprocessing on the airborne terminal according to the image transmission link status parameters to obtain the latest image and transmit it. S3. Utilize the ground receiver to dynamically enhance the latest received images.

2. The airborne image processing method based on long-distance image transmission according to claim 1, characterized in that, S2 includes the following sub-steps: S21. Divide the original image into blocks to obtain several image blocks; S22. Collect the image transmission link status parameters for transmitting the original image, and calculate the signal transmission interference value of the original image based on several image blocks. S23. Obtain the image transmission link status parameters of historical images, calculate the signal transmission interference value of each historical image, and set the average signal transmission interference value of all historical images as the transmission threshold. S24. When the signal transmission interference value is greater than the transmission threshold, the image block is subjected to Gaussian kernel function weighted fusion processing; when the signal transmission interference value is less than or equal to the transmission threshold, the image block is subjected to mean filtering.

3. The airborne image processing method based on long-distance image transmission according to claim 2, characterized in that, S21 includes the following sub-steps: S221. Acquire the image transmission link status parameters for transmission; wherein, the image transmission link status parameters include the image block operating frequency and modulation phase; S222. Based on the image block image transmission link status parameters, the transmission energy coefficient of the image block during the single block transmission duration is determined using a cosine function. S223. Input the negative of the ratio between the minimum transmission energy coefficient and the maximum transmission energy coefficient into the exponential function as the interference indicator weight of the original image. S224. Calculate the signal transmission interference value of the original image based on the interference indication weight of the original image.

4. The airborne image processing method based on long-distance image transmission according to claim 3, characterized in that, In S222, the transmission energy coefficient The expression is: ； in, Indicates the first The operating frequency of each image block Indicates the first The modulation phase of each image block, This indicates the transmission power of the image transmission link. This indicates the number of image patches in the original image. Indicates the start time of image block transmission.

5. The airborne image processing method based on long-distance image transmission according to claim 3, characterized in that, In S224, the signal transmission interference value of the original image The expression is: ； in, The interference indicator weights represent the original image. This indicates the transmission power of the interference signal. This represents the white Gaussian noise at the ground receiver. This represents the channel gain from the airborne terminal to the ground receiver. This indicates the transmission power of the image transmission link. This represents the channel gain of the interference signal.

6. The airborne image processing method based on long-distance image transmission according to claim 2, characterized in that, In step S24, the Gaussian kernel weighted fusion process for the image blocks includes the following sub-steps: S241. Acquire the gray-level co-occurrence matrix of the original image and the local gray-level co-occurrence matrix of each image block; S242. Determine the entropy difference of the image blocks based on the gray-level co-occurrence matrix of each local gray-level matrix and the gray-level co-occurrence matrix of the original image; S243. Perform clustering on all entropy differences to determine the clusters corresponding to the image patches; S244. The ratio between the entropy difference of the image patch and the center value of the cluster to which the image patch belongs is used as the adjustment weight of the Gaussian kernel; S245. The variance of the Gaussian kernel is optimized by adjusting the weights of the Gaussian kernel, and the optimized Gaussian kernel is used to complete the weighted fusion processing of the Gaussian kernel function on the image patch.

7. The airborne image processing method based on long-distance image transmission according to claim 6, characterized in that, In step S242, the absolute value of the difference between the entropy of each local gray-level co-occurrence matrix and the entropy of the gray-level co-occurrence matrix of the original image is calculated as the entropy difference of the image block.

8. The airborne image processing method based on long-distance image transmission according to claim 1, characterized in that, In step S3, adaptive gamma correction is used to dynamically enhance the received latest image.

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