Research On Reinforcement Learning Fusion Method Of Multi Focus Images

Multi-focus image fusion technology is to fuse images obtained from different sensors from the same scene into an image,remove redundant information and extract useful information,so as to obtain an image which contains more information,more clearly and easier to handle.The application of multi-focus image fusion technology covers many aspects,such as aerospace,medical imaging,safety monitoring,and so on.This paper mainly focuses on the research of multi-focus image fusion quality evaluation,reinforcement learning and multi-focus image fusion algorithm.The aim of this paper is to apply the popular reinforcement learning algorithm to multi-focus image fusion so as to obtain better fusion results.The main work includes the following aspects:1.in view of the problem that It is difficult to deal with the problem of image fuzzy distortion by the fusion quality evaluation method of structural similarity.According to the contrast information is the most important part of the multi-focus image,the similarity of local variance similarity and contrast similarity are introduced on the basis of structural similarity.At the same time,with the introduction of human visual attention mechanism,a multi-scale composite structure similarity for multi-focus image fusion quality evaluation method based on visual attention mechanism is proposed.It is more suitable for the evaluation of the quality for multi-focus image fusion,and the effectiveness and superiority of the method are verified by experimental analysis.2.in view of the problem that the current image fusion algorithm can not achieve good fusion effect on all images,the Q-learning method in reinforcement learning is introduced,and an reinforcement learning fusion algorithm for multi-focus images is proposed.This method takes the joint multi-features between the source images as the state of the source image.The image fusion quality evaluation method proposed in this paper is used as the return function.At the same time,the four algorithms which have better fusion effect on the multi-focus image are used as four actions,untill the Q function is converged by training.for any set of multi-focus images,a relatively optimal fusion algorithm can be found to achieve better fusion results by the Q function.Through a large number of experimental analysis,the effectiveness and practical significance of the algorithm are verified.3.the combination of fractional wavelet transform and guided filter is applied to multi focus image fusion,and the method of reinforcement learning is introduced to solve the problem that the fractional order wavelet transform is difficult to determine the order.This method can get the best fractional order for any group of multi-focus source images,thusachieving better fusion results.The superiority and robustness of the algorithm are verified by experimental analysis.