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
February 2011, 28 pages.
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Status: available - back from printing
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.
URL to this document: https://domino.mpi-inf.mpg.de/internet/reports.nsf/NumberView/2011-4-002
BibTeX
@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},
}