Image Inpainting Based on Probabilistic Structure Estimation

Abstract

A novel inpainting method based on probabilistic structure estimation has been developed. The method consists of two steps. First, an initial image, which captures rough structure and colors in the missing region, is estimated. This image is generated by probabilistically interpolating the gradient inside the missing region, and then by flooding the colors on the boundary into the missing region using Markov Random Field. Second, by locally replacing the missing region with local patches similar to both the adjacent patches and the initial image, the inpainted image is synthesized. Since the patch replacement process is guided by the initial image, the inpainted image is guaranteed to preserve the underlying structure. This also enables patches to be replaced in a greedy manner, i.e. without optimization. Experiments show the proposed method outperforms previous methods in terms of both subjective image quality and computational speed.KeywordsMarkov Random FieldInitial ImageMissing RegionSubjective Image QualityGreedy MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.