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MPI-I-2011-4-002

Efficient learning-based image enhancement : application to compression artifact removal and super-resolution

Kim, Kwang In and Kwon, Younghee and Kim, Jin Hyung and Theobalt, Christian

MPI-I-2011-4-002. February 2011, 28 pages. | Status: available - back from printing | Next --> Entry | Previous <-- Entry

Abstract in LaTeX format:
Many computer vision and computational photography applications
essentially solve an image enhancement problem. The image has been
deteriorated by a specific noise process, such as aberrations from camera
optics and compression artifacts, that we would like to remove. We
describe a framework for learning-based image enhancement. At the core of
our algorithm lies a generic regularization framework that comprises a
prior on natural images, as well as an application-specific conditional
model based on Gaussian processes. In contrast to prior learning-based
approaches, our algorithm can instantly learn task-specific degradation
models from sample images which enables users to easily adapt the
algorithm to a specific problem and data set of interest. This is
facilitated by our efficient approximation scheme of large-scale Gaussian
processes. We demonstrate the efficiency and effectiveness of our approach
by applying it to example enhancement applications including single-image
super-resolution, as well as artifact removal in JPEG- and JPEG
2000-encoded images.
Acknowledgement:
References to related material:

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Hide details for BibTeXBibTeX
@TECHREPORT{KimKwonKimTheobalt2011,
  AUTHOR = {Kim, Kwang In and Kwon, Younghee and Kim, Jin Hyung and Theobalt, Christian},
  TITLE = {Efficient learning-based image enhancement : application to
compression artifact removal and super-resolution},
  TYPE = {Research Report},
  INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik},
  ADDRESS = {Stuhlsatzenhausweg 85, 66123 Saarbr{\"u}cken, Germany},
  NUMBER = {MPI-I-2011-4-002},
  MONTH = {February},
  YEAR = {2011},
  ISSN = {0946-011X},
}