Infrared and Visible Image Fusion Based on RPCA and NSST

Aiming at the problem that the traditional image fusion method is not easy to highlight the target information and the background information is not sufficient, an infrared and visible image fusion algorithm based on robust principal component analysis (RPCA) and non-downsampling Shearlet transform (NSST) is proposed. Firstly, the infrared and visible images are decomposed by RPCA, and the corresponding sparse matrices are obtained. Then, the infrared and visible images are decomposed in multi-scale and multi-direction by NSST transform to obtain the corresponding high-frequency and low-frequency components. The high frequency part obtained above adopts the fusion method of combining large absolute value with sparse matrix, and the low frequency part adopts the fusion method of sparse matrix to guide the low frequency part. Finally, the fused image is obtained by inverse NSST transform of each component. The experimental results show that compared with other image fusion algorithms, the algorithm can obtain fused images with more prominent targets and richer background information.