Hyperspectral Images Mixed Noise Removal via Group-Tube Transform Induced Collaborative Sparsity and Low-Rankness

The transform-based tensor nuclear norm (TNN) methods have shown promise in hyperspectral images (HSIs) mixed noise removal. However, these transform-based methods only consider the low-rankness of the transformed tensor. The sparsity of the transformed tensor, which is beneficial for HSIs denoising, is neglected in the transform-based TNN methods. In this paper, we propose a novel HSIs denoising model based on the group-tube transform induced collaborative sparsity and low-rankness (GTCSLR). Here, GTC-SLR organically integrates the sparsity and low-rankness, capitalizing on their synergies to enhance denoising performance. We develop a proximal alternating minimization (PAM)-based algorithm to efficiently address the resulting optimization problem. Experimental results demonstrate the superiority of our method over state-of-the-art HSIs denoising methods, as evidenced by quality metrics and visual assessments.