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Image interpolation via graph-based bayesian label propagation
Liu X.1; Zhao D.1; Zhou J.2; Gao W.3,4; Sun H.5
2014
Source PublicationIEEE Transactions on Image Processing
ISSN10577149
Volume23Issue:3Pages:1084
Abstract

In this paper, we propose a novel image interpolation algorithm via graph-based Bayesian label propagation. The basic idea is to first create a graph with known and unknown pixels as vertices and with edge weights encoding the similarity between vertices, then the problem of interpolation converts to how to effectively propagate the label information from known points to unknown ones. This process can be posed as a Bayesian inference, in which we try to combine the principles of local adaptation and global consistency to obtain accurate and robust estimation. Specially, our algorithm first constructs a set of local interpolation models, which predict the intensity labels of all image samples, and a loss term will be minimized to keep the predicted labels of the available low-resolution (LR) samples sufficiently close to the original ones. Then, all of the losses evaluated in local neighborhoods are accumulated together to measure the global consistency on all samples. Moreover, a graph-Laplacian-based manifold regularization term is incorporated to penalize the global smoothness of intensity labels, such smoothing can alleviate the insufficient training of the local models and make them more robust. Finally, we construct a unified objective function to combine together the global loss of the locally linear regression, square error of prediction bias on the available LR samples, and the manifold regularization term. It can be solved with a closed-form solution as a convex optimization problem. Experimental results demonstrate that the proposed method achieves competitive performance with the state-of-the-art image interpolation algorithms. © 2013 IEEE.

KeywordGlobal Consistency Graph Image Interpolation Label Propagation Local Adaptation Regression
DOI10.1109/TIP.2013.2294543
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000335389600002
The Source to ArticleScopus
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Citation statistics
Cited Times [WOS]:30   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorLiu X.
Affiliation1.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
2.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Taipa, Peoples R China
3.Peking Univ, Sch Elect Engn & Comp Sci, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China
4.Peking Univ, Sch Elect Engn & Comp Sci, Key Lab Machine Percept, Beijing 100871, Peoples R China
5.Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA
Recommended Citation
GB/T 7714
Liu X.,Zhao D.,Zhou J.,et al. Image interpolation via graph-based bayesian label propagation[J]. IEEE Transactions on Image Processing,2014,23(3):1084.
APA Liu X.,Zhao D.,Zhou J.,Gao W.,&Sun H..(2014).Image interpolation via graph-based bayesian label propagation.IEEE Transactions on Image Processing,23(3),1084.
MLA Liu X.,et al."Image interpolation via graph-based bayesian label propagation".IEEE Transactions on Image Processing 23.3(2014):1084.
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