High-Order Nonparametric Belief-Propagation for Fast Image Inpainting

Simple item page
dc.creatorMcAuley, Julian John
dc.creatorCaetano, Tiberio S.
dc.date2007-10-01
dc.date.accessioned2026-07-07T08:33:16Z
dc.date.available2026-07-07T08:33:16Z
dc.descriptionIn 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.
dc.description8 pages, 6 figures
dc.identifierhttps://arxiv.org/abs/0710.0243
dc.identifierhttp://arxiv.org/abs/0710.0243
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/139071
dc.subjectComputer Vision and Pattern Recognition
dc.titleHigh-Order Nonparametric Belief-Propagation for Fast Image Inpainting
dc.typetext

Files

Collections

arXiv