Image Fusion Based on NSST and CSR Under Robust Principal Component Analysis

Abstract

Aiming at the problems of loss of detail information and noise interference that are easy to produce in the image fusion process, a robust principal component analysis (RPCA) based on Convolutional Sparse Coding (CSR) and For image fusion of NonSubsampled Shear Wave Transform (NSST), the source image is pre-enhanced first; then the image is decomposed by RPCA to obtain low-rank images and sparse images; then NSST fusion is used respectively For low-rank images, CSR coding is used to fuse sparse images, and finally two separately fused images are synthesized to obtain the final fused image. Experimental results show that the algorithm in this paper can effectively improve the contrast and clarity of the fused image, reduce noise interference, rich scene information, clear targets, and overall objective evaluation indicators are better than existing algorithms, and the operating efficiency has also been improved