Learning How to Inpaint from Global Image Statistics

Inpainting is the problem of filling-in holes in images. Considerable progress has been made by techniques that use the immediate boundary of the hole and some prior information on images to solve this problem. These algorithms successfully solve the local inpainting problem but they must, by definition, give the same completion to any two holes that have the same boundary, even when the rest of the image is vastly different. In this paper we address a different, more global inpainting problem. How can we use the rest of the image in order to learn how to inpaint? We approach this problem from the context of statistical learning. Given a training image we build an exponential family distribution over images that is based on the histograms of local features. We then use this image specific distribution to inpaint the hole by finding the most probable image given the boundary and the distribution. The optimization is done using loopy belief propagation. We show that our method can succe...

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Anat Levin, Assaf Zomet, Yair Weiss
• Keywords: Computer Vision, Global Inpainting Problem, ICCV 2003, Image Specific Distribution, Local Inpainting Problem, Loopy Belief Propagation, Specific Image Statistics