Automatic Parametrisation for an Image Completion Method Based on Markov Random Fields

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.