Automatic Parametrisation for an Image Completion Method Based on
Markov Random Fields
Authors: Huy Tho Ho and Roland Göcke
Presented by Huy Tho Ho at the 2007 IEEE International Conference
on Image Processing ICIP2007, San Antonio (TX), USA, 16-19 September
2007.
Abstract
Recently, a new exemplar-based method for image completion,
texture synthesis and image inpainting was proposed
which uses a discrete global optimization strategy based on
Markov Random Fields. Its main advantage lies in the use
of priority belief propagation and dynamic label pruning to
reduce the computational cost of standard belief propagation
while producing high quality results. However, one of the
drawbacks of the method is its use of a heuristically chosen
parameter set. In this paper, a method for automatically determining
the parameters for the belief propagation and dynamic
label pruning steps is presented. The method is based on an
information theoretic approach making use of the entropy of
the image patches and the distribution of pairwise node potentials.
A number of image completion results are shown
demonstrating the effectiveness of our method.
AUTHOR = {H.T. Ho and R. Goecke},
TITLE = {{Automatic Parametrisation for an Image Completion Method Based on Markov Random Fields}},
BOOKTITLE = {{Proceedings of the 2007 IEEE International Conference on Image Processing ICIP2007}},
PUBLISHER = {IEEE},
ADDRESS = {San Antonio (TX), USA},
VOLUME = 3,
PAGES = {541--544},
MONTH = sep,
YEAR = 2007}