Hyperspectral image denoising with segmentation-based low rank representation

Abstract

Recently, low-rank representation (LRR) based hyperspectral image (HSI) denoising method has been proven to be a powerful tool for removing different kinds of noise simultaneously, such as Gaussian, dead pixels and impulse noise. However, the LRR based method cannot make full use of the spatial information in HSI. In this paper, we integrate the graph based segmentation (GS) into the LRR, and propose a novel denoising method named GS-LRR. We first use the principle component analysis (PCA) to obtain the first principle component of HSI. Then the graph based segmentation is adopted to the first principle component of HSI to get homogeneous regions. Finally, we employ the LRR to each homogeneous region of HSI, which enable us to simultaneously remove all the above mentioned mixed noise. Extensive experiments on both simulated and real HSIs demonstrate the efficiency of the proposed GS-LRR.