Local Low-rank and Sparse Representation for Hyperspectral Image Denoising

In this paper, instead of adopting the global low-rank property, we propose to adopt a local low rankness for HSI de-noising. Hyperspectral image (HSI) de-noising is a fundamental task in a plethora of HSI applications. Global low-rank property is widely adopted to exploit the spectral-spatial information of HSIs, providing satisfactory de-noising results. 

We develop an HSI de-noising method via local low-rank and sparse representation, under an alternative minimization framework. In addition, the weighted nuclear norm is used to enhance the sparsity on singular values. The experiments on widely used hyperspectral datasets demonstrate that the proposed method outperforms several state-of-the-art methods visually and quantitatively.