红外技术2024，Vol.46Issue(9)：1025-1034,10.
基于非凸低秩张量分解和群稀疏总变分的高光谱混合噪声图像恢复
Hyperspectral Mixed Noise Image Restoration Based on Non-Convex Low-Rank Tensor Decomposition and Group Sparse Total Variation
摘要
Abstract
Hyperspectral images(HSIs)are polluted by a large amount of mixed noise during the acquisition process,which affects the performance of subsequent applications of remote sensing images.Therefore,restoring clean HSI from the mixed noise is an important preprocessing step.In this study,a hyperspectral mixed noise image restoration model based on nonconvex low-rank tensor decomposition and group-sparse total variational regularization is proposed.On the one hand,by using logarithmic tensor nuclear norm to approximate the low-rank characteristics of the HSI,the inherent tensor structure of hyperspectral data can be utilized,and the shrinkage of larger singular values can be reduced to preserve more detailed features of the image.On the other hand,the group sparse total variational regularization can be used to enhance the spatial sparsity of the HSI and correlation between adjacent spectra.ADMM algorithm is used to solve the problem,and an experiment shows that the algorithm converges easily.In simulated and real hyperspectral image experiments,this method performs better in removing HSI mixed noise when compared to other methods.关键词
高光谱图像/混合噪声/非凸低秩张量分解/群稀疏总变分/图像恢复Key words
hyperspectral image/mixed noise/non-convex low-rank tensor decomposition/group sparse total variation/image restoration分类
计算机与自动化引用本文复制引用
徐光宪,王泽民,马飞..基于非凸低秩张量分解和群稀疏总变分的高光谱混合噪声图像恢复[J].红外技术,2024,46(9):1025-1034,10.基金项目
国家科技攻关项目(2018YFB1403303) （2018YFB1403303）
辽宁省基础研究项目(LJ2020JCL012) （LJ2020JCL012）
辽宁省教育厅科学研究面上项目(LJKZ0357) （LJKZ0357）
辽宁省科技厅自然科学基金计划面上项目(2023-MS-314). （2023-MS-314）
