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.
关键词
张量核范数/高光谱图像/张量环分解/空间光谱全变分
Key words
tensor kernel norms/hyperspectral images/tensor ring decomposition/spatial-spectral total variation
维普
万方数据