Image fusion method based on adaptive group structure sparse dictionary learning

Abstract

The invention discloses an image fusion method based on a sparse representation self-adaptive learning group structured dictionary, specifically: inputting an image to be fused and performing sliding window segmentation, extracting feature vectors to obtain training samples to train an adaptive group structure dictionary, Calculate the sparse representation vector for each image block, take the sparse representation vector corresponding to the same position and use the group structure-based L1 norm maximization method to obtain the fused coefficient representation vector matrix, and finally add the corresponding mean value to convert it into an image Indicates that the image block is obtained, and the inverse operation of the sliding window is used to output the final fusion image. The fused image of the present invention is more accurate and has richer details, and can reduce the spatial discontinuity of the fused image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular relates to fusion of images, and can be used in the fields of image processing, computer vision, medical image processing, military affairs and the like. Background technique [0002] In recent years, sparse representation has received more and more attention as an effective method, and it has been applied to many problems in image processing, such as: image denoising, image fusion, image compression, etc. A scene contains a wealth of information, and a single imaging sensor system cannot capture all the information in the scene. In order to solve this problem, a multi-sensor image fusion system emerged as the times require. The multi-sensor image fusion system effectively utilizes the complementarity between different imaging sensors It can eliminate redundant information between multi-sensor images, synthesize images acquired by different imaging sensors, and form a more ...

AI Technical Summary

Problems solved by technology

However, the K-SVD algorithm has the disadvantages of constructing a dictionary: the representation ability of a single dictionary is limited, and the ability to represent the details of the image is insufficient, which will result in smoother images after fusion and poor reconstruction ability for details.

[0006] (1) Some significant information needs to be preserved in the image block with a smaller L1 norm, and such a fusion rule will not obtain this part of information in the fused image, making the information loss of the fused image relatively large. Big, not rich enough detail

[0007] (2) The rule with the largest L1 norm will cause spatial discontinuity in the fusion image

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