Learning Reaction-Diffusion Models for Image Inpainting
Wei Bo Yu, Stefan Heber, Thomas Pock · Lecture notes in computer science · 2015
In this paper we present a trained diffusion model for image inpainting based on the structural similarity measure. The proposed diffusion model uses several parametrized linear filters and influence functions. Those parameters are learned in a loss based approach, where we first perform a greedy training before conducting a joint training to further improve the inpainting performance. We provide a detailed comparison to state-of-the-art inpainting algorithms based on the TUM-image inpainting database. The experimental results show that the proposed diffusion model is efficient and achieves superior performance. Moreover, we also demonstrate that the proposed method has a texture preserving property, that makes it stand out from previous PDE based methods. These 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.