High-Order Nonparametric Belief-Propagation for Fast Image Inpainting

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.