Noise reduction of hyperspectral imagery based on hypergraph laplacian regularized low-rank representation

Low-rank representation is one of the state-of-art hyperspectral image denoising algorithms, but it suffers from ignoring the high-order relationships between data points in image. In this paper, we propose a hypergraph laplacian regularized low-rank representation algorithm for noise reduction of hyperspectral images, which can represent the high-order relations between data points by using the hypergraph laplacian regularization. On the other hand, to further improve the ability to maintain the local information, the sparse and non-negative constraints have been added to the model coefficient matrix. The proposed method not only resumes the low-rank signal components, but also represents the high-order relations of the image data. The experimental results on AVIRIS and ProSpecTIR-VS datasets show that the proposed approach canbetter maintain the spatial and spectral information of images, which improves the hyperspectral image denoising results.