Image Renaissance Using Discrete Optimization

In this paper we propose a novel technique to image completion that addresses image renaissance through a graph-based matching process. To this end, a number of candidate seeds with content similar to the one of the area to be inpainted are considered. They are selected through a particle filter method and then positioned over the missing area. Markov random fields are used to formalize inpainting as a labeling estimation problem while a combinatorial approach is used to recover the optimal partition of patches that completes the missing area with the alpha-expansion process. Promising results in image and texture completion demonstrate the potentials of the proposed method