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 successfully complete holes while taking into
account the specific image statistics. In particular it can give vastly
different completions even when the local neighborhoods are identical.