NDLI: Face hallucination via weighted sparse representation

Content Provider	IEEE Xplore Digital Library
Author	Zhongyuan Wang Junjun Jiang Zixiang Xiong Ruimin Hu Zhenfeng Shao
Copyright Year	2013
Description	Author affiliation: LIESMARS, Wuhan Univ., Wuhan, China (Zhenfeng Shao) \|\| Sch. of Comput., NERCMS, China (Zhongyuan Wang; Junjun Jiang; Ruimin Hu) \|\| Texas A&M Univ., College Station, TX, USA (Zixiang Xiong)
Abstract	By incorporating the priors of image positions, position-patch based face hallucination methods can produce high-quality results and save computation time. These methods represent the test image patch as a linear combination of the same position patches in a training dictionary, and the key issue is how to obtain the optimal coefficients. Due to stability and accuracy issues, methods based on least square estimation or sparse representation (SR) proposed so far are not satisfactory. In this paper, we improve existing SR methods by exploiting similarity between the test and training patches. In particular, we impose a similarity constraint (in terms of the distance between the test patch and bases in the dictionary) on the $ℓ_{1}$ minimization regularization term and obtain the coefficients by solving a weighted SR problem. We also provide a new prospective on weighted SR and investigate its robustness to illumination variations. Experiments on commonly used database demonstrate that our method outperforms state of the art.
Starting Page	2198
Ending Page	2201
File Size	841206
Page Count	4
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638044
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	PSNR Abstracts Lighting Indexes Noise measurement position patches Super-resolution face hallucination weighted sparse representation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Face hallucination based on stepwise sparse reconstruction

Position-Patch Based Face Hallucination via Locality-Constrained Representation

Face Hallucination Via Weighted Adaptive Sparse Regularization

Face hallucination via Cauchy regularized sparse representation

Face hallucination VIA sparse coding

Noise Robust Face Hallucination via Locality-Constrained Representation

Robust face super-resolution via position-patch neighborhood preserving

Facial Image Super Resolution Using Weighted Patch Pairs

Face hallucination via sparse coding (2008)

Face hallucination via weighted sparse representation

Similar Documents

Face hallucination based on stepwise sparse reconstruction

Position-Patch Based Face Hallucination via Locality-Constrained Representation

Face Hallucination Via Weighted Adaptive Sparse Regularization

Face hallucination via Cauchy regularized sparse representation

Face hallucination VIA sparse coding

Noise Robust Face Hallucination via Locality-Constrained Representation

Robust face super-resolution via position-patch neighborhood preserving

Facial Image Super Resolution Using Weighted Patch Pairs

Face hallucination via sparse coding (2008)

Face hallucination via weighted sparse representation