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应用科学学报 >

2022 , Vol. 40 >Issue 1: 80 - 92

DOI: https://doi.org/10.3969/j.issn.0255-8297.2022.01.008

计算机应用专辑

基于NSCT与压缩感知的红外影像融合

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  • 1. 东华理工大学 信息工程学院, 江西 南昌 330013;
    2. 东华理工大学 江西省核地学数据科学与系统工程技术研究中心, 江西 南昌 330013

收稿日期: 2021-07-12

  网络出版日期: 2022-01-28

基金资助

国家自然科学基金（No.41862012）；江西省核地学数据科学与系统工程技术研究中心开发基金（No.JETRCNGDSS201801）资助

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Infrared Image Fusion Based on NSCT and Compressed Sensing

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  • 1. School of Information Engineering, East China University of Technology, Nanchang 330013, Jiangxi, China;
    2. Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, Jiangxi, China

Received date: 2021-07-12

  Online published: 2022-01-28

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摘要

针对红外和可见光图像在融合过程中存在质量低下、信息缺失、边缘细节不突出等问题，提出一种基于非下采样轮廓波变换（non-subsampled contourlet transform，NSCT）与稀疏表示的压缩感知图像融合重构算法。首先利用NSCT进行源图像分解，得到相应的高频子带和低频子带图像；然后针对高频子带部分，利用基于压缩感知的高频融合规则进行融合，得到高频融合系数；针对低频子带部分，按照基于字典学习的低频融合规则进行融合，得到低频融合系数。最后进行NSCT逆变换得到融合影像，实现红外和可见光图像的超分辨率恢复。实验结果表明：采用该算法融合后的图像在平均梯度、边缘强度、信息熵、边缘信息保留度、空间频率等指标上均有良好的表现，体现出该融合算法在图像融合质量的提升方面颇具优势。

关键词： 图像融合; 非下采样轮廓波变换; 稀疏表示; 压缩感知; 红外影像

本文引用格式

金安安, 李祥, 张丽, 熊卿智 . 基于NSCT与压缩感知的红外影像融合[J]. 应用科学学报, 2022 , 40(1) : 80 -92 . DOI: 10.3969/j.issn.0255-8297.2022.01.008

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

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