T
Ting-Zhu Huang
Researcher at University of Electronic Science and Technology of China
Publications - 497
Citations - 8614
Ting-Zhu Huang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Linear system & Iterative method. The author has an hindex of 38, co-authored 490 publications receiving 6158 citations. Previous affiliations of Ting-Zhu Huang include Central China Normal University & Chongqing Jiaotong University.
Papers
More filters
Proceedings ArticleDOI
A Novel Tensor-Based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors
TL;DR: A novel tensor based video rain streaks removal approach by fully considering the discriminatively intrinsic characteristics of rain streaks and clean videos, which needs neither rain detection nor time-consuming dictionary learning stage is proposed.
Journal ArticleDOI
Deblurring and Sparse Unmixing for Hyperspectral Images
TL;DR: According to the structure of the TV regularization and sparse unmixing in the model, the convergence of the alternating direction method can be guaranteed and the method is compared to the recent Sparse Unmixing via variable Splitting Augmented Lagrangian and TV method.
Journal ArticleDOI
Mixed Noise Removal in Hyperspectral Image via Low-Fibered-Rank Regularization
TL;DR: A fibered rank minimization model for HSI mixed noise removal is proposed, in which the underlying HSI is modeled as a low-fibered-rank component and each subproblem within ADMM is proven to have a closed-form solution, although 3DLogTNN is nonconvex.
Journal ArticleDOI
Tensor completion using total variation and low-rank matrix factorization
TL;DR: A block coordinate decent (BCD) algorithm is developed to efficiently solve the proposed optimization model combining the total variation regularization and low-rank matrix factorization and theoretically shows that under some mild conditions the algorithm converges to the coordinatewise minimizers.
Journal ArticleDOI
Image restoration using total variation with overlapping group sparsity
TL;DR: This work extends the total variation with overlapping group sparsity, which was previously developed for one dimension signal processing, to image restoration and proposes an efficient algorithm for solving the corresponding minimization problem.