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Recently, low-rank representation (LRR) based hyperspectral image (HSI) restoration method has been proven to be a powerful tool for simultaneously removing different types of noise, such as Gaussian, dead pixels and impulse noise. However, the LRR based method just adopts the square patch denoising strategy, which makes it not able to excavate the spatial information in HSI. This paper integrates...
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...
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...
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