A multi-source image fusion method based on reinforcement learning

Abstract

The invention relates to the field of image processing, in particular to a multi-source image fusion method based on reinforcement learning. The method decomposes a multi-source image to obtain a high-frequency coefficient signal and a low-frequency coefficient signal, and performs weighted reconstruction on the high-frequency coefficient signal and the low-frequency coefficient signal to obtain a fusion image; the method adopts Q-learning in reinforcement learning The algorithm trains the weights of the high-frequency coefficient signal and the low-frequency coefficient signal, takes the weight when the fusion evaluation criterion is optimal, performs weighted fusion to obtain the fusion coefficient signal, and reconstructs the fusion image according to the fusion coefficient signal. Based on the Q-learning algorithm, the weights of high-frequency coefficient signals and low-frequency coefficient signals from multi-source image decomposition are trained, which improves the reliability and accuracy of weight estimation.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a multi-source image fusion method based on reinforcement learning. Background technique [0002] In recent years, military drones have been widely used in combat reconnaissance and precision strikes due to their unique advantages. With the development of military high-tech, the scope of the UAV battlefield has expanded to land, sea, air and electromagnetic and other multi-dimensional spaces. The battlefield environment has become increasingly complex. More and more attention has been paid to the multi-source image fusion technology that integrates the information of multiple image sensors to obtain a comprehensive and detailed description of the same target or scene. [0003] Multi-source image fusion technology is an important content of UAV intelligence processing technology. Its core idea is to integrate multi-source images obtained by multiple sensors under different conditi...

AI Technical Summary

Problems solved by technology

[0006] 1) In UAV aerial reconnaissance, the images acquired by multi-image sensors are susceptible to external interference such as atmosphere, light and shaking, resulting in a decrease in the quality of the acquired images;

[0007] 2) Different fusion methods have different emphases on image data, and the accuracy of fusion is also different, and the fusion results may not obtain ideal results;

[0008] 3) In the process of image decomposition, it is easy to lose information or generate redundant information, and the signal reconstruction stability in the fusion process is poor;

[0009] In the process of image fusion and reconstruction, the reliability and accuracy of the weighted weight estimation of fusion coefficients need to be improved

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