T
Tony F. Chan
Researcher at Hong Kong University of Science and Technology
Publications - 437
Citations - 51198
Tony F. Chan is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Domain decomposition methods & Image restoration. The author has an hindex of 82, co-authored 437 publications receiving 48083 citations. Previous affiliations of Tony F. Chan include Kent State University & University of California.
Papers
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Journal ArticleDOI
Fast dual minimization of the vectorial total variation norm and applications to color image processing
Xavier Bresson,Tony F. Chan +1 more
TL;DR: In this paper, the authors proposed a vectorial extension of the vectorial total variation (VTV) norm for gray-scale/scalar images, which is fast, easy to code and mathematically well-posed.
Recent Developments in Total Variation Image Restoration
TL;DR: There has been a resurgence of interest and exciting new developments in total variation minimizing models, some extending the applicabilities to inpainting, blind deconvolution and vector-valued images, while others offer improvements in better preservation of contrast, geometry and textures.
Proceedings ArticleDOI
Level set based shape prior segmentation
Tony F. Chan,Wei Zhu +1 more
TL;DR: A level set based variational approach that incorporates shape priors into Chan-Vese's model for the shape prior segmentation problem and provides a proof for a fast solution principle, which was mentioned by F. Gibou et al., (2002) and extended to the minimization of the prescribed functionals.
Proceedings ArticleDOI
Make it home: automatic optimization of furniture arrangement
TL;DR: A system that automatically synthesizes indoor scenes realistically populated by a variety of furniture objects is presented and whether there is a significant difference in the perceived functionality of the automatically synthesized results relative to furniture arrangements produced by human designers is investigated.
Proceedings ArticleDOI
A level set algorithm for minimizing the Mumford-Shah functional in image processing
Tony F. Chan,Luminita A. Vese +1 more
TL;DR: In this paper, the authors show how the piecewise-smooth Mumford-Shah segmentation problem can be solved using the level set method, which can be simultaneously used to denoise, segment, detect-extract edges, and perform active contours.