Compressed Fusion of Infrared and Visible Images Combining Robust Principal Component Analysis and Non-Subsampled Contour Transform

Existing fusion algorithms for infrared and visible images face issues such as low contrast and clarity of fused image and loss of detailed texture information. To address these problems, a fusion algorithm combining robust principal component analysis (RPCA), compressed sensing (CS), and non-subsampled contour transform (NSCT) is proposed. Firstly, two original images are pre-enhanced, and the pre-enhanced images are decomposed via RPCA to obtain the corresponding sparse and low-rank components. Secondly, the sparse matrices are compressed and sampled using the structural random matrix. Gauss gradient-differential contrast of information (GG-DCI) is used to compress and fuse the images, and the reconstruction is conducted using the orthogonal matching tracking method (OMP). Then the low-rank matrices are decomposed into low- and high-frequency components via NSCT. The low-frequency components are fused using the regional energy-intuitionistic fuzzy set (RE-IFS), the highest-frequency components are fused using the maximum absolute value rule, and other high-frequency components are fused using the adaptive Gaussian region variance. Finally, the fused images are obtained by superimposing the fused sparse and low-rank components. Experimental results show that compared with other algorithms, the proposed algorithm can more effectively improve the contrast and clarity of fused images, retain abundant detailed texture information, possess generally better objective evaluation indexes, and efficiently improve the fusion effect of infrared and visible images.