Multi-focus image fusion method based on sparse decomposition and differential image

Abstract

The invention provides a multi-focus image fusion method based on sparse decomposition and a differential image. The multi-focus image fusion method is characterized by comprising the steps of utilizing RPCA to decompose an original image to obtain its sparse component and low-rank component, enhancing an edge of the sparse component through guide filtration, adding the enhanced sparse component to the low-rank component to construct an enhanced image comprising a strong edge, performing differential processing on the enhanced image and the original image, calculating spatial frequency of an obtained differential image, utilizing a self-adaptive threshold algorithm to obtain a spatial frequency diagram of the differential image, utilizing a morphological algorithm to remove a pseudo-focus region in the spatial frequency diagram to obtain a fusion decision-making diagram, and reconstructing a fusion image according to the fusion decision-making diagram and a fusion rule. Three index of the multi-focus image fusion method in evaluating an image fusion algorithm, namely a correlation coefficient, edge gradient information and an edge correlation factor are higher than that of other classical algorithms by more than 45%, 6% and 15% respectively.

Description

technical field [0001] The present invention relates to a technology in the field of image processing, in particular to a multi-focus image fusion method based on sparse decomposition and difference images. Background technique [0002] The visible light imaging system focuses on the objects in the scene by adjusting the depth of field of the optical lens, and cannot clearly express all the objects in the complex scene at the same time. The multi-focus image fusion algorithm uses the complementarity between the original images to merge the focus areas of multiple original images to obtain a clear fusion image, which can describe the target or scene in the image more accurately and comprehensively, effectively improving the utilization rate and reliability of the original image. sex. [0003] The target in the image is characterized by geometric features. Although the regional feature extraction method represented by the multi-resolution analysis method can extract the conto...

AI Technical Summary

Problems solved by technology

However, this technology cannot identify the clear area of ​​the image, and the filtering operation will lose edge information. It is only applicable to CT sequence images that are severely disturbed by noise, and cannot be used for multi-focus image fusion.

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