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Xiyou Fu
Researcher at Shenzhen University
Publications - 8
Citations - 186
Xiyou Fu is an academic researcher from Shenzhen University. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 3, co-authored 5 publications receiving 25 citations. Previous affiliations of Xiyou Fu include Ontario Ministry of Natural Resources.
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Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations
TL;DR: The proposed factorization hinges on the optimal shrinkage/thresholding of the singular value decomposition (SVD) singular values of low-rank tensor unfoldings of nonlocal similar 3-D patches, thus greatly improving the denoising performance and reducing the computational complexity during processing.
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Hyperspectral Anomaly Detection via Deep Plug-and-Play Denoising CNN Regularization
TL;DR: A novel anomaly detection algorithm that uses a plug-and-play prior for representation coefficients and constructs a new dictionary based on clustering and a modified background dictionary construction method, which carefully includes background pixels and excludes anomalous pixels from clustering results.
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Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations
TL;DR: Robust hyperspectral denoising (RhyDe) as mentioned in this paper is a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and preserves rare pixels.
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Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior
TL;DR: In this paper, the authors estimate clean HSIs from observations corrupted by mixed noise by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain.
Journal ArticleDOI
Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations
TL;DR: Robust hyperspectral denoising (RhyDe) as mentioned in this paper is a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and preserves rare pixels.