Hyperspectral Image Restoration Algorithm Based on Tensor Ring Decomposition and Spatial-spectral Total Variation
Hyperspectral images were subject to various pollutants during acquisition and conversion,and most noise removal algo-rithms currently used on Tucker or CP could alter the inherent structure of the signal,making it very difficult to estimate the opti-mal tensor rank.To this end,a hyperspectral image restoration model based on tensor ring decomposition tensor and spatial-spectral total variation was proposed:low rank was constrained by tensor ring decomposition tensor kernel norm and spatial-spectral total variation to better explore the global spatial structure and spectral correlation of adjacent bands,and the augmented Lagrangian algorithm was used to solve this model.Numerical experiments show that the model produces clear images after remov-ing noise,and the PSNR,SSIM,and FSIM metrics are all superior to existing algorithms.
tensor kernel normshyperspectral imagestensor ring decompositionspatial-spectral total variation
万方数据
维普
