基于NSCT与压缩感知的红外影像融合

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...

Infrared Image Fusion Based on NSCT and Compressed Sensing

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.