Hyperspectral Image Mixed Noise Removal Based on Sparsity Constraint and Low-Rank Approximation

Hyperspectral images (HSIs) are often corrupted by various noise during acquisition, which leads to a reduction in HSI quality. Therefore, HSI denoising is a necessary preprocessing step for their further application. In this paper, we propose an HSI denoising framework based on the sparsity constraint and low-rank approximation (SCLA). SCLA simultaneously uses the global low-rank structure, sparse prior, and local spatial-spectral smoothness of HSIs to construct a denoising model for removal of heterogeneous Gaussian noise, impulse noise, and stripe mixed noise. The Split Bregman method is used for SCLA to solve the optimization problem of our model. An ablation study is utilized to show the effectiveness of the structures of SCLA. Compared with other denoising methods, based on simulated and real experiments, SCLA achieves significant advantages in quantitative and visual assessments for removing Gaussian noise, impulse noise, and stripe mixed noise.