HYPERSPECTRAL IMAGE DENOISING VIA SPECTRAL AND SPATIAL LOW-RANK APPROXIMATION

Hyperspectral images (HSI) unavoidably suffer from degradations such as random noise, due to photon effects, calibration error, and so on. Most of existing HSI denoising methods focus on utilizing the spectral correlation or the spatial non local self-similarity individually. In this paper, we propose an unified low-rank recovery framework for HSI denoising, in which taking both the underlying characteristics of high correlation across spectra and non-local self-similarity over the space cubic of HSI into consideration simultaneously. Our work rely on a basic observation that both the multiple spectral bands and similar spatial structures are lying on low-rank subspaces and can facilitate to remove the noise jointly. Experimental results on both simulated and real HSI demonstrate that the proposed method can significantly outperform the state-of-the-art methods on several datasets in terms of both visual and quantitative assessment.