University of Illinois Urbana-Champaign

Learning image super resolution from joint examples

Wang, Zhangyang

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Zhangyang_Wang.pdf

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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
Date of Ingest
2015-01-21T19:58:59Z
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.
Graduation Semester
2014-12
Permalink
http://hdl.handle.net/2142/73058
Copyright and License Information
Copyright 2014 Zhangyang Wang

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