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.Comment: 8 pages, 6 figure

Applications of Graphical Models: Image Inpainting using Belief-Propagation

v vi4.1 Pseudocode for the EM algorithm......................... 39 4.2 Pseudocode for the K-means clustering algorithm................. 40 5.1 Topology of a scratched region........................... 45 6.1 Regions for which the junction-tree algorithm may or may not be used..... 51 6.2 Inpainting results: removing text from an image................. 53 6.3 Inpainting results: comparison with state-of-the-art................ 54 6.4 Inpainting results: comparison of different models................. 55 6.5 Inpainting results: inpainting a colour image................... 56 6.6 Two equally large regions to be inpainted, in two differently sized images... 57 7.1 Correcting corrupted regions using a noise-model................. 60 A.1 Transformation of a Gaussian............................ 64 viii List of Tables 6.1 Comparison of different models........................... 54 6.2 Number of operations required by our algorithm................. 5