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Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in HSIs. Recently,...
In this paper, the problem of balancing the noise removing and the image details preserving is considered. To remove noise adaptively, local dictionaries and sparse coding techniques are used. For a noised image patch, the local dictionary corresponding to it and the sparse coding technique are used to generate the sparse coding vector of the given patch. Then the noise of the given patch can be removed...
The core of image denoising is making a trade-off between removing noise and preserving details of noised image. To remove noise, the denoising algorithm based on K-SVD is employed in this paper. Though the power of such denoising algorithm has been verified by a mount of experiments, many meaningful details of noised image cannot be well maintained. To preserve details of noised image, therefore,...
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