Abstract
Hyperspectral images are often degraded by various types of noise. It is important to choose appropriate image priors for the underdetermined imaging denoising problems. Graph Laplacian regularizer is one popular prior which can reveal the inherent relationship between signals and exhibits desirable PWS-preserving properties. In this paper, we combine the Graph Singal Processing theory (GSP) with HSI low-rank-based denoising methods. We classify the noise on HSI into three types, and treat the clean image and different types of noise as independent components. The simulated and real-data experiments demonstrate that the proposed denoising methods outperforms many of the mainstream methods in both the quantitative evaluation indexes and visual effects.
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