Mixed Noise Removal for Hyperspectral Images Based on Global Tensor Low-Rankness and Nonlocal SVD-Aided Group Sparsity
Le Sun, Qiujie Cao, Yuwen Chen, Yuhui Zheng, Zebin Wu
Abstract
In hyperspectral images (HSIs), mixed noise ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., Gaussian noise, impulse noise, stripe noise, and deadlines) contamination is a common phenomenon that greatly reduces the visual quality of the image. In recent years, methods combining global and non-local low-rankness have been widely used in the field of HSI denoising. However, most methods apply original space-based denoising strategies (low-rank tensor decomposition, total variation, and tensor sparse representation, etc.) directly to the modeling of non-local low-rank tensors in subspace, without fully exploiting the intrinsic and latent properties of the non-local similar tensors. In this paper, we propose a hybrid prior denoising method based on global tensor low-rankness and non-local SVD-aided group sparsity (GTL_NSGS). This method introduces a novel plug-and-play NSGS denoiser that uses singular value decomposition as assistance to successively explore self-similarity of spatial dimension, low-rankness of spectral dimension, and group sparsity of difference domain in subspace non-local similar tensors. Globally, we utilize the existing three-way log-based tensor nuclear norm (3DLogTNN) to approximate the HSI tensor fibered rank and introduce a difference continuity regularization to obtain a continuous smooth spectral basis. Finally, we combine the Alternating Direction Method of Multipliers (ADMM) with the Augmented Lagrangian Multiplication (ALM) algorithm to solve the proposed model effectively. Extensive experiments on simulated and real data sets demonstrate that the proposed method has superior performance in removing mixed noise compared to state-of-the-art denoisers.