CASIA OpenIR
Iterative Relaxed Collaborative Representation With Adaptive Weights Learning for Noise Robust Face Hallucination
Liu, Licheng1; Li, Shutao1; Chen, C. L. Philip2,3,4
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN1051-8215
2019-05-01
Volume29Issue:5Pages:1284-1295
Corresponding AuthorLiu, Licheng(lichenghnu@gmail.com) ; Li, Shutao(shutao_li@hnu.edu.cn)
AbstractIn recent years, the collaborative representation (CR)-based techniques have been widely employed for face hallucination. However, the conventional CR model becomes less efficient in handling noisy low-resolution face images. In this paper, an iterative relaxed CR (iRCR) model with adaptive weights learning is presented to enhance the resolution of face images corrupted by noise. The core idea of iRCR is that a diagonal weight matrix is incorporated into the objective function, which helps to debase the influence of noise in representation. Different from existing collaborative methods with reweighting strategy where the weights require manually tuning, the weights in iRCR are adaptively learned to stay more consistent with the model error. Moreover, considering the local manifold structure property and nonlocal prior of small patches, the locality regularization and collaborative regularization are incorporated into a unified framework. This enables the proposed iRCR not only to capture the true topology structure of patch manifold but also to exploit the meaningful patterns among the whole training samples for reconstruction. Experimental results on both face dataset and real-world images demonstrate the superiority of our proposed method over several state-of-the-art face hallucination methods.
KeywordRelaxed collaborative representation face hallucination locality regularization adaptively weights learning noise robust coding
DOI10.1109/TCSVT.2018.2829758
WOS KeywordIMAGE SUPERRESOLUTION ; SPARSE REPRESENTATION ; RECOGNITION ; ALGORITHM ; EQUATIONS ; SYSTEMS ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61702169] ; National Natural Science Foundation of China[61572540] ; National Natural Science Foundation of China[61751202] ; National Natural Science Fund of China for International Cooperation and Exchanges[61520106001] ; Fundamental Research Funds for the Central Universities[531107050878]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Fund of China for International Cooperation and Exchanges ; Fundamental Research Funds for the Central Universities
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000467063100005
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24590
Collection中国科学院自动化研究所
Corresponding AuthorLiu, Licheng; Li, Shutao
Affiliation1.Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
2.Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China
3.Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Liu, Licheng,Li, Shutao,Chen, C. L. Philip. Iterative Relaxed Collaborative Representation With Adaptive Weights Learning for Noise Robust Face Hallucination[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2019,29(5):1284-1295.
APA Liu, Licheng,Li, Shutao,&Chen, C. L. Philip.(2019).Iterative Relaxed Collaborative Representation With Adaptive Weights Learning for Noise Robust Face Hallucination.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,29(5),1284-1295.
MLA Liu, Licheng,et al."Iterative Relaxed Collaborative Representation With Adaptive Weights Learning for Noise Robust Face Hallucination".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 29.5(2019):1284-1295.
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