Spectral-spatial hyperspectral image destriping using low-rank representation and Huber-Markov random fields

This paper presents a novel spectral-spatial destriping method for hyperspectral images. The ubiquitous striping noise in hyperspectral images might degrade the quality of the imagery and bring difficulties in hyperspectral data processing. Although numerous methods have been proposed for striping noise reduction recently, most of them fail to consider the spectral correlation and spatial information of the hyperspectral images simultaneously. In order to remedy this drawback, the proposed method integrates the spectral and spatial information to remove the striping noise in the hyperspectral images. To this end, firstly, the low-rank representation (LRR) is used to take advantage of the spectral information. Then, the spatial information is included using a Huber-Markov random field (MRF) prior model, which is convex and can well preserve the edge and texture information while removing the noise. The experimental results on simulated and real hyperspectral data sets demonstrate the effectiveness of the proposed method.