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• W2963213461 endingPage "578" @default.
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• W2963213461 abstract "Deep learning has been successfully introduced for 2D-image denoising, but it is still unsatisfactory for hyperspectral image (HSI) denoising due to the unacceptable computational complexity of the end-to-end training process and the difficulty of building a universal 3D-image training dataset. In this paper, instead of developing an end-to-end deep learning denoising network, we propose an HSI denoising framework for the removal of mixed Gaussian impulse noise, in which the denoising problem is modeled as a convolutional neural network (CNN) constrained non-negative matrix factorization problem. Using the proximal alternating linearized minimization, the optimization can be divided into three steps: the update of the spectral matrix, the update of the abundance matrix, and the estimation of the sparse noise. Then, we design the CNN architecture and proposed two training schemes, which can allow the CNN to be trained with a 2D-image dataset. Compared with the state-of-the-art denoising methods, the proposed method has a relatively good performance on the removal of the Gaussian and mixed Gaussian impulse noises. More importantly, the proposed model can be only trained once by a 2D-image dataset but can be used to denoise HSIs with different numbers of channel bands." @default.
• W2963213461 created "2019-07-30" @default.
• W2963213461 creator A5009073713 @default.
• W2963213461 creator A5050785912 @default.
• W2963213461 creator A5072435188 @default.
• W2963213461 date "2020-01-01" @default.
• W2963213461 modified "2023-10-15" @default.
• W2963213461 title "Hyperspectral Image Denoising via Matrix Factorization and Deep Prior Regularization" @default.
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• W2963213461 doi "https://doi.org/10.1109/tip.2019.2928627" @default.
• W2963213461 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31329115" @default.
• W2963213461 hasPublicationYear "2020" @default.
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