Low-rank tensor completion with spatial-spectral consistency for hyperspectral image restoration  

作　　者：XIAO Zhiwen ZHU Hu 

机构地区：[1]School of Communications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China

出　　处：《Optoelectronics Letters》2023年第7期432-436,共5页光电子快报（英文版）

摘　　要：Hyperspectral image(HSI) restoration has been widely used to improve the quality of HSI.HSIs are often impacted by various degradations,such as noise and deadlines,which have a bad visual effect and influence the subsequent applications.For HSIs with missing data,most tensor regularized methods cannot complete missing data and restore it.We propose a spatial-spectral consistency regularized low-rank tensor completion(SSC-LRTC) model for removing noise and recovering HSI data,in which an SSC regularization is proposed considering the images of different bands are different from each other.Then,the proposed method is solved by a convergent multi-block alternating direction method of multipliers(ADMM) algorithm,and convergence of the solution is proved.The superiority of the proposed model on HSI restoration is demonstrated by experiments on removing various noises and deadlines.

关 键 词：TENSOR CONSISTENCY removing 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]