M
Michael K. Ng
Researcher at University of Hong Kong
Publications - 658
Citations - 24376
Michael K. Ng is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 72, co-authored 608 publications receiving 20492 citations. Previous affiliations of Michael K. Ng include The Chinese University of Hong Kong & Vanderbilt University.
Papers
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Heuristics approach to printed circuit board insertion problem
TL;DR: This paper proposes a heuristic solution technique of low computational complexity to find a better assembly plan comprising of the assembly sequence of electronic components and the placement order of the reels in the feeder.
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Fast minimization methods for solving constrained total-variation superresolution image reconstruction
TL;DR: The main purpose of this paper is to develop an inexact alternating direction method for solving constrained TV image reconstruction problem and Experimental results are given to show that the proposed algorithm is effective and efficient.
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On algorithms for automatic deblurring from a single image
Wei Wang,Michael K. Ng +1 more
TL;DR: In this paper, two variational blind deblurring models for a single image are proposed, one model is to use the total variation prior in both image and blur, while the second model is using the frame based prior.
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Nonconvex Optimization for Robust Tensor Completion from Grossly Sparse Observations
TL;DR: This paper proposes and develops a nonconvex model, which minimizes a weighted combination of tubal nuclear norm, the $$\ell _1$$ -norm data fidelity term, and a concave smooth correction term and presents a Gauss–Seidel difference of convex functions algorithm (GS-DCA) to solve the resulting optimization model by using a linearization technique.
Posted Content
Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery.
TL;DR: Wang et al. as mentioned in this paper proposed a nonlinear multilayer neural network to learn a non-linear transform via the observed tensor data under self-supervision, which makes use of low-rank representation of transformed tensors and data-fitting between the observed and reconstructed tensors to construct the nonlinear transformation.