Hyperspectral image denoising via low-rank matrix recovery

Based on low-rank matrix recovery theory, we propose a novel method to remove the hyperspectral image noise. To robustly handle the outliers in hyperspectral images, we first build a hybrid noise model for the hyperspectral images. Then, the noise removal is achieved via two stages. In the first stage, the main fine-image features are first separated from the noise via principal component analysis (PCA) due to its good performance in signal/noise decorrelation. In the second stage, the noise removal is conducted in the low-energy PCA channels through low-rank matrix recovery because of its strong capability in dealing with badly corrupted matrices. The experimental results on both simulated and real data validated the effectiveness of the proposed method both visually and quantitatively.