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Wenfei Cao
Researcher at Shaanxi Normal University
Publications - 7
Citations - 355
Wenfei Cao is an academic researcher from Shaanxi Normal University. The author has contributed to research in topics: Tensor & Robust principal component analysis. The author has an hindex of 6, co-authored 7 publications receiving 220 citations. Previous affiliations of Wenfei Cao include Xi'an Jiaotong University.
Papers
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Nonconvex-Sparsity and Nonlocal-Smoothness-Based Blind Hyperspectral Unmixing
TL;DR: This paper proposes novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NL HTV- LSR NMF), respectively, and an efficient algorithm is designed based on an alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM).
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Fast image deconvolution using closed-form thresholding formulas of Lq(q=12,23) regularization
Wenfei Cao,Jian Sun,Zongben Xu +2 more
TL;DR: Based on the closed-form formulas for L"q(q=12,23) regularization, a fast algorithm to solve the image deconvolution problem using half-quadratic splitting method is proposed and extensive experiments demonstrate that the algorithm has a significant acceleration over Krishnan et al.'s algorithm.
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Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization
Dong Zeng,Qi Xie,Wenfei Cao,Jiahui Lin,Hao Zhang,Shanli Zhang,Jing Huang,Zhaoying Bian,Deyu Meng,Zongben Xu,Zhengrong Liang,Wufan Chen,Jianhua Ma +12 more
TL;DR: A new DCPCT image reconstruction algorithm is proposed to improve low-dose DCP CT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor.
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Destriping Remote Sensing Image via Low-Rank Approximation and Nonlocal Total Variation
TL;DR: This letter considers the nonlocal self-similarity of image patches in the spatiospectral volume in terms of nonlocal total variation and proposes a method of better robustness to dense stripes that outperforms other competing methods in the remote sensing image destriping task.
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A Robust PCA Approach With Noise Structure Learning and Spatial–Spectral Low-Rank Modeling for Hyperspectral Image Restoration
TL;DR: This paper proposes a novel robust principal component analysis approach for mixed noise removal by fully identifying the intrinsic structures of the mixed noise and clean HSI and develops an efficient algorithm for the resulting optimization problem.