Polarization difference and light intensity image multi-scale fusion method based on edge information enhancement

Abstract

The invention provides a polarization difference and light intensity image multi-scale fusion method based on edge information enhancement. The method comprises: obtaining a polarization difference image and a light intensity image through a minimum mutual information polarization difference imaging method and polarization information analysis; secondly, denoising the light intensity image by adopting a three-dimensional block matching filtering algorithm, and enhancing the light intensity image by adopting a guide filtering algorithm; affine transformation and three-dimensional block matchingfiltering algorithm denoising are carried out on the polarization difference image; decomposing the light intensity image and the polarization difference image into a high-frequency coefficient and alow-frequency coefficient by adopting double-tree complex wavelet transform; the high-frequency coefficient images in different directions on different decomposition layers in the high-frequency coefficients adopt a fusion rule based on edge detection, and the low-frequency coefficient images in different directions in the low-frequency coefficients adopt a fusion rule based on regional varianceand variance matching degree; And obtaining a fused image through even complex wavelet inverse transformation.

Description

technical field [0001] The invention relates to an image processing technology, in particular to a multi-scale fusion method of polarization difference and light intensity image based on edge information enhancement. Background technique [0002] Target detection in complex backgrounds is an important research topic in the military and civilian fields. Image fusion can use redundant and complementary information between images to highlight useful information in the image, making the image description of the same scene more accurate and comprehensive. Therefore, image fusion technology is the basis for subsequent target detection, recognition and tracking in complex backgrounds. [0003] At present, image fusion techniques are generally divided into two categories, image fusion based on spatial domain and image fusion based on transform domain. The former is carried out on the pixel gray value, such as weighted average, principal component analysis, etc.; the latter first pe...

AI Technical Summary

Problems solved by technology

However, wavelet transform still has certain limitations. Due to the downsampling process in processing, wavelet transform does not have translation invariance

In addition, the direction selectivity of wavelet transform is poor, and it can only provide detailed information in horizontal, vertical and diagonal directions, which is not conducive to the capture of image direction information.

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