基于NSCT与压缩感知的红外影像融合

doi:10.3969/j.issn.0255-8297.2022.01.008

Abstract

Abstract: Aiming at the problems of low quality, lack of information and non-prominent edge details in the fusion process of infrared and visible images, this paper proposes a compressed sensing image fusion and reconstruction algorithm based on non-subsampled contourlet transform (NSCT) and sparse representation. Firstly, a source image is decomposed by using NSCT to obtain corresponding high-frequency sub-band and low-frequency sub-band images. Then, the high-frequency sub-band images are fused by using the highfrequency fusion rules based on compressed sensing to obtain high-frequency fusion coefficients. For the low-frequency sub-band images, low-frequency fusion coefficients are obtained by using the low-frequency fusion rules based on dictionary learning. Finally, a fusion image is obtained by using the inverse NSCT transformation to achieve superresolution recovery of infrared and visible images. Experimental results show that the images fused by this algorithm have good performance in metrics, such as average gradient, edge intensity, information entropy, edge information retention and spatial frequency, and prove that this fusion algorithm has significant advantages in image fusion quality.

Key words: image fusion, non-subsampled contour transform (NSCT), sparse representation, compressed sensing (CS), infrared image

CLC Number:

TP751
TN219

JIN An'an, LI Xiang, ZHANG Li, XIONG Qingzhi. Infrared Image Fusion Based on NSCT and Compressed Sensing[J]. Journal of Applied Sciences, 2022, 40(1): 80-92.

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Infrared Image Fusion Based on NSCT and Compressed Sensing

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