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

Abstract:  Hyperspectral images have significant reference value in fields such as environmental monitoring， remote sensing science， 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 structural 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. Furthermore， 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.

Key words: hyperspectral images, image denoising, group sparsity, mixed noise, alternating direction method of multiplier