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Title:Learning image super resolution from joint examples
Author(s):Wang, Zhangyang
Advisor(s):Huang, Thomas S.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
example-based learning
sparse coding
epitomic matching
subject evaluation
Abstract:Image super-resolution (SR) aims to estimate of a high-resolution (HR) image from low-resolution (LR) input. Image priors are commonly learned to regularize the ill-posed SR problem, either using external LR-HR pairs or internal similar patterns repeating across di erent scales. We propose joint SR to adaptively combine the advantages of both external and internal SR. We de ne the two loss functions using sparse coding and epitomic matching, respectively. A corresponding adaptive weight is constructed to balance their e ect according to the reconstruction errors. Various image results demonstrate the e ectiveness of the proposed method over the existing state-of-the-art methods, which is also veri ed by our subject evaluation experiment.
Issue Date:2015-01-21
Rights Information:Copyright 2014 Zhangyang Wang
Date Available in IDEALS:2015-01-21
Date Deposited:2014-12

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