NDLI: Learning local pixel structure for face hallucination

Content Provider	IEEE Xplore Digital Library
Author	Yu Hu Kin Man Lam Guoping Qiu Tingzhi Shen Hui Tian
Copyright Year	2010
Description	Author affiliation: School of Computer Science, University of Nottingham, UK (Guoping Qiu) \|\| Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong (Kin Man Lam) \|\| Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, 100081, China (Yu Hu; Tingzhi Shen; Hui Tian)
Abstract	In this paper, we present a novel learning-based face hallucination method based on the assumption that similar faces will have similar local pixel structures. We use the low- resolution (LR) input face to search a database for K example faces that are the most similar to the input and align them with the input accordingly. The local pixel structures of the target high-resolution (HR) image are learned from those warped HR example faces in a neighbor embedding manner, and a total variation (TV) constraint is employed to aid the learning of all pixels' embedding weights. The learned local pixel structures are then used as constraints to reconstruct a HR version of the input face. Experimental results show that the method performs well in terms of both reconstruction error and visual quality.
Starting Page	2797
Ending Page	2800
File Size	504946
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5651052
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Pixel Image reconstruction Strontium TV Image resolution Training super resolution face hallucination local pixel structure TV norm
Content Type	Text
Resource Type	Article

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