Abstract
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.
Original language | English |
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Title of host publication | Pattern Recognition |
Subtitle of host publication | 37th German Conference, GCPR 2015, Aachen, Germany, October 7-10, 2015, Proceedings |
Publisher | Springer International Publishing AG |
Pages | 356-367 |
Volume | 9358 |
ISBN (Electronic) | 978-3-319-24947-6 |
ISBN (Print) | 978-3-319-24946-9 |
DOIs | |
Publication status | Accepted/In press - 2015 |
Fields of Expertise
- Information, Communication & Computing