Learning image super resolution from joint examples
Wang, Zhangyang
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https://hdl.handle.net/2142/73058
Description
Title
Learning image super resolution from joint examples
Author(s)
Wang, Zhangyang
Issue Date
2015-01-21
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas S.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
super-resolution
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.
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