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Wei He
Researcher at Wuhan University
Publications - 124
Citations - 3665
Wei He is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & Hyperspectral imaging. The author has an hindex of 18, co-authored 50 publications receiving 1791 citations. Previous affiliations of Wei He include German Aerospace Center.
Papers
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Hyperspectral Image Restoration Using Low-Rank Matrix Recovery
TL;DR: A new HSI restoration method based on low-rank matrix recovery (LRMR), which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes, is introduced.
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Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration
TL;DR: A spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV) that integrates the nuclear norm, TV regularization, and L1-norm together in a unified framework for HSI restoration.
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Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation
TL;DR: A noise-adjusted iterative low-rank matrix approximation (NAILRMA) method is proposed for HSI denoising that can effectively preserve the high- SNR bands and denoise the low-SNR bands.
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Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing
TL;DR: In the multiplicative iterative solution to the proposed TV-RSNMF model, the TV regularizer can be regarded as an abundance map denoising procedure, which improves the robustness of TV- RSNMF to noise.
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Interpretable Hyperspectral Artificial Intelligence: When nonconvex modeling meets hyperspectral remote sensing
Danfeng Hong,Wei He,Naoto Yokoya,Jing Yao,Lianru Gao,Liangpei Zhang,Jocelyn Chanussot,Xiao Xiang Zhu +7 more
TL;DR: Compared to convex models, nonconvex modeling, which is capable of characterizing more complex real scenes and providing model interpretability technically and theoretically, has proven to be a feasible solution that reduces the gap between challenging HS vision tasks and currently advanced intelligent data processing models.