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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Fast preconditioned iterative methods for finite volume discretization of steady-state space-fractional diffusion equations
TL;DR: A scaled-circulant preconditioner is proposed to use to deal with Toeplitz-like discretization matrices of steady-state variable-coefficient conservative space-fractional diffusion equations and it is demonstrated that the preconditionsed Krylov subspace method converges very quickly.
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Retinex image enhancement via a learned dictionary
TL;DR: The main aim of this paper is to study image enhancement by using sparse and redundant representations of the reflectance component in the Retinex model over a learned dictionary to provide better visual quality of the enhanced high-contrast images than the other variational methods.
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Variational fuzzy Mumford–Shah model for image segmentation
TL;DR: A variational fuzzy Mumford–Shah model for image segmentation based on the assumption that an image can be approximated by the product of a smooth function and a piecewise constant function is proposed.
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A Variational Method for Multiple-Image Blending
Wei Wang,Michael K. Ng +1 more
TL;DR: An algorithm for blending of multiple images in the image-stitching process containing an energy functional to determine both a stitched image and weighting mask functions of multiple input images for image blending is developed.
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The Best Circulant Preconditioners for Hermitian Toeplitz Systems
TL;DR: A new family of circulant preconditioners for ill-conditioned Hermitian Toeplitz systems A x= b is proposed, constructed by convolving the generating function f of A with the generalized Jackson kernels and it is shown that the convergence is superlinear.