NDLI: Face hallucination via Cauchy regularized sparse representation

Content Provider	IEEE Xplore Digital Library
Author	Shenming Qu Ruimin Hu Shihong Chen Zhongyuan Wang Junjun Jiang Cheng Yang
Copyright Year	2015
Description	Author affiliation: Comput. Sch., Nat. Eng. Res. Center for Multimedia Software, Wuhan Univ., Wuhan, China (Shenming Qu; Ruimin Hu; Shihong Chen; Zhongyuan Wang; Junjun Jiang; Cheng Yang)
Abstract	In dictionary-learning-based face hallucination, the testing image is represented as a linear combination of the training samples, and how to obtain the optimal coefficients is the primary issue. Sparse representation (SR) has ever been widely used in face hallucination, however, due to the fact that SR overemphasizes the sparsity, the obtained linear combination coefficients turn out far aggressively sparse, then leading to unsatisfactory hallucinated results. In this paper, we present a moderately sparse prior model for face hallucination problem with the L1 norm penalty in classic SR replaced by a Cauchy penalty term. An iterative optimization is further presented to solve the minimization of Cauchy regularized objective function. The experimental results on public face database demonstrate that our method is much more effective than state-of-the-art methods.
Starting Page	1216
Ending Page	1220
File Size	829311
Page Count	5
File Format	PDF
ISBN	9781467369978
DOI	10.1109/ICASSP.2015.7178163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Training Databases Image resolution Dictionaries Signal resolution Image reconstruction Cauchy regularization Super-resolution face hallucination sparse representation
Content Type	Text
Resource Type	Article

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