Hallucinating faces: TensorPatch super-resolution and coupled residue compensation
Refereed conference paper presented and published in conference proceedings


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AbstractIn this paper, we propose a new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models. Considering that image patches reflect the combined effect of personal characteristics and patch-location, we first formulate a TensorPatch model based on multilinear analysis to explicitly model the interaction between multiple constituent factors. Motivated by Locally Linear Embedding, we develop an enhanced multilinear patch hallucination algorithm, which efficiently exploits the local distribution structure in the sample space. To better preserve face subtle details, we derive the Coupled PCA algorithm to learn the relation between high-resolution residue and low-resolution residue, which is utilized for compensate the error residue in hallucinated images. Experiments demonstrate that our framework on one hand well maintains the global facial structures, on the other hand recovers the detailed facial traits in high quality.
All Author(s) ListLiu W, Lin DH, Tang X
Name of ConferenceConference on Computer Vision and Pattern Recognition
Start Date of Conference20/06/2005
End Date of Conference25/06/2005
Place of ConferenceSan Diego
Country/Region of ConferenceUnited States of America
Year2005
Month1
Day1
PublisherIEEE COMPUTER SOC
Pages478 - 484
ISBN0-7695-2372-2
ISSN1063-6919
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology

Last updated on 2020-02-04 at 02:06