Anisotropic Spatial–Spectral Total Variation Regularized Double Low-Rank Approximation for HSI Denoising and Destriping

Abstract

Hyperspectral images (HSIs) can finely discriminate distinct objects with a high spectral resolution, and they are widely employed in various applications. However, mixed noise severely degrades the quality of HSI and restricts the performance of subsequent tasks. As one of the critical pre-processing steps, HSI denoising has been developed rapidly, among which low-rank (LR) prior-based methods have achieved superior performance. Nevertheless, the existing approaches frequently fail to completely remove noise and reconstruct high-quality HSIs when tackling complicated mixed noise with multiple types of high-intensity stripe noise. To solve this problem, we propose an HSI denoising and destriping method based on anisotropic spatial and spectral total variation regularized double low-rank approximation (ATVDLR). The double low-rank approximation framework is devoted to separating the clean image from the mixed noise by exploiting both the global correlations of HSI tensor and the LR structure of stripe noise. Furthermore, the anisotropic spatial and spectral total variation regularization is introduced to preserve the spatial–spectral smoothness of HSI and the directional feature of stripes, thereby further suppressing high-level stripes and Gaussian noise. Finally, the alternating direction method of multipliers (ADMM) technique is designed to solve the proposed ATVDLR model. Extensive experimental results indicate that the proposed method outperforms other state-of-the-art techniques in multi-type high-intensity mixed noise reduction and image structural information protection, and has superior performance in complex mixed noise removal of real Gaofen-5 HSIs.