Hyperspectral image denoising with multiscale low-rank matrix recovery

Abstract

Recently, low-rank matrix recovery has been demonstrated to be an effective tool in hyperspectral images (HSIs) denoising. However, the previous low-rank matrix recovery method with a window of the fixed-shape cannot adaptively exploit spatial structure information and nonlocal similarity. In this paper, multiscale low-rank matrix recovery (MC-LRMR) is proposed to recover HSI corrupted by different kinds of noise. The proposed method contains three main steps. First, HSI is transformed by the principal component analysis (PCA) algorithm and multiscale superpixel segmentation is applied to the first principal component, to segment the HSI into non-overlapping homogeneous regions. Then, the mixed noises, including Gaussian noise, impulse noise, dead lines noise, stripes noise, are removed by the low-rank matrix recovery (LRMR) in a superpixel-by-superpixel manner. Finally, a fusion rule, i.e., average operator, is adopted to combine denoising results of various scales, to acquire a fused noise-free estimation. Experiments on simulated and real HSI data sets can demonstrate the effectiveness of the proposed method.