Denoising of Hyperspectral Image Using Low-Rank Matrix Factorization

Restoration of hyperspectral images (HSIs) is a challenging task, owing to the reason that images are inevitably contaminated by a mixture of noise, including Gaussian noise, impulse noise, dead lines, and stripes, during their acquisition process. Recently, HSI denoising approaches based on low-rank matrix approximation have become an active research field in remote sensing and have achieved state-of-the-art performance. These approaches, however, unavoidably require to calculate full or partial singular value decomposition of large matrices, leading to the relatively high computational cost and limiting their flexibility. To address this issue, this letter proposes a method exploiting a low-rank matrix factorization scheme, in which the associated robust principal component analysis is solved by the matrix factorization of the low-rank component. Our method needs only an upper bound of the rank of the underlying low-rank matrix rather than the precise value. The experimental results on the simulated and real data sets demonstrate the performance of our method by removing the mixed noise and recovering the severely contaminated images.