  In this paper, we use belief-propagation techniques to develop fast
algorithms for image inpainting. Unlike traditional gradient-based approaches,
which may require many iterations to converge, our techniques achieve
competitive results after only a few iterations. On the other hand, while
belief-propagation techniques are often unable to deal with high-order models
due to the explosion in the size of messages, we avoid this problem by
approximating our high-order prior model using a Gaussian mixture. By using
such an approximation, we are able to inpaint images quickly while at the same
time retaining good visual results.
