计算机与现代化Issue(1)：30-36,112,8.DOI:10.3969/j.issn.1006-2475.2025.01.006
基于低秩张量分解与加权组稀疏的高光谱图像去噪
Hyperspectral Image Denoising Using Low Rank Tensor Decomposition and Weighted Group Sparse Regularization
摘要
Abstract
Hyperspectral images have significant reference value in fields such as environmental monitoring,remote sensing sci-ence,and medical imaging.However,the imaging process is susceptible to contamination by mixed noise due to limitations in the imaging acquisition equipment and adverse weather conditions,leading to a significant decline in image quality.To tackle this problem,we propose a denoising model for hyperspectral images based on low rank tensor decomposition and weighted group sparsity-regularized.Specifically,to effectively retain the edge information of the hyperspectral image and extract sparse struc-tural features,we propose a group sparse regularization method based on the l2,1 norm,which aims to weight and constrain the differential images in the spatial and spectral directions.Then,a combined approach is proposed,which utilizes the l1 norm and Frobenius norm,to effectively eliminate complex mixed noise in the images,thereby enhancing the overall image quality.Further-more,we use ADMM algorithm to solve the model proposed in this paper.Experimental evaluations of the model are conducted using both simulated and real data,and the results demonstrate the superiority of the proposed model over the baseline model in terms of various evaluation metrics,particularly the proposed model has obvious advantages in hyperspectral image recovery.关键词
高光谱图像/图像去噪/组稀疏/混合噪声/交替方向乘子法Key words
hyperspectral images/image denoising/group sparsity/mixed noise/alternating direction method of multiplier分类
信息技术与安全科学引用本文复制引用
王叶芳,贾小宁,成丽波,李喆..基于低秩张量分解与加权组稀疏的高光谱图像去噪[J].计算机与现代化,2025,(1):30-36,112,8.基金项目
国家自然科学基金资助项目(12171054) （12171054）
吉林省教育厅科学技术研究项目(JJKH20230788KJ) （JJKH20230788KJ）
