Hyperspectral image denoising with bilinear low rank matrix factorization

Abstract

Hyperspectral images (HSIs) have rich spectral information, but the various noises generated during the imaging process destroy the visual quality of images and lower the application precision. Therefore, it's crucial to denoise HSI for making better use of it. At present, low-rank-based methods have shown potential in mixture noises removal. While their limitations in rank function approximation, which affects the description of the low rank property in HSI, still need to be broken through. This paper puts forward a bilinear low rank matrix factorization (BLRMF) HSI denoising method, where the bi-nuclear quasi-norm is employed for constraining the low rank characteristic in HSI. The bi-nuclear quasi-norm is a closer approximation to the rank function and can be calculated by the nuclear norms of two smaller factor matrices, which respectively describe the spatial low rank and the spectral low rank. The Alternating Direction Method of Multipliers (ADMM) is employed for solving the optimization problem. A large number of experiments on HSI denoising are conducted to verify the superiority of the BLRMF over the mainstream denoising methods.