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 -expansion process. Promising results in image and texture completion demonstrate the potentials of the proposed method.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Cédric Allène, Nikos Paragios
• Keywords: Computer Vision, Graph-based Matching Process, ICPR 2006, Missing Area, Particle Filter Method