Hyperspectral image denoising based on low-rank representation and superpixel segmentation

Abstract

Recently, low-rank representation (LRR) based methods have been used for hyperspectral image (HSI) denoising, which can simultaneously remove different types of noise: Gaussian noise, impulse noise, dead lines, and so on. However, the LRR based method does not make full use of the spatial information in HSI. In this paper, we integrate the superpixel segmentation (SS) into the LRR, and propose a novel denoising method named SS-LRR. We first use the principle component analysis (PCA) to obtain the first principle component of HSI. Then the superpixel 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 hyperspectral images demonstrate that the proposed SS-LRR is efficient for HSI denoising.