Image Renaissance Using Discrete Optimization and the α-Expansion Algorithm

In this paper we propose a novel technique that addresses image renaissance through a ”multi-level” graph-based matching process. To this end, numerous image patches that do present similarities with the local content around the missing part are considered. The selection of these patches is done through a particle filter method to address the task of hypotheses evaluation. These patches are positioned on top of missing segment, ordered depending on their similarity weight, and form in some fashion a multi-layered graph over time. Markov Random Fields are used to formalize inpainting as a labelling estimation problem while a combinatorial approach is used to recover the optimal combination of patches to complete the missing structure. The min-cut max-flow algorithm within the α-expansion process is used to determine the optimal cut that, in an implicit fashion, completes the missing image structure. Promising results in image and texture completion demonstrate the potentials of the proposed method.