Name
Abstract | ||
|---|---|---|
A novel, fully automatic framework for restoration of unknown or damaged picture areas is presented. Diverse causes as an accident, manual removal, or transmission loss may have lead to the missing visual information. The challenge then consists in repairing the occluded or missing image regions in an undetectable way. Here, assumption is made that dominant structures are of salient relevance to the human perception. Hence, they are accounted for in the filling process by using tensor voting, which is a structure inference approach based on the Gestalt laws of proximity and good continuation. In fact, based on a new segmentation-based inference mechanism presented in this paper, missing textures crossing dominant structures are robustly recovered. An efficient post-processing step based on cloning via covariant derivatives improves the visual quality of the inpainted textures. The proposed method yields significantly better results than previous approaches. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WIAMIS.2009.5031465 | London |
Keywords | Field | DocType |
image restoration,image segmentation,image texture,Gestalt laws,cloning,complex image content,covariant derivatives,filling process,fully automatic inpainting method,human perception,image restoration,missing textures crossing dominant structures,missing visual information,post-processing step,segmentation-based inference mechanism,tensor voting,visual quality | Computer vision,Pattern recognition,Segmentation,Visualization,Inference,Image texture,Computer science,Gestalt psychology,Inpainting,Image segmentation,Artificial intelligence,Image restoration | Conference |
ISBN | Citations | PageRank |
978-1-4244-3610-1 | 4 | 0.44 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Köppel | 1 | 4 | 0.44 |
| Dimitar Doshkov | 2 | 4 | 0.44 |
| Patrick Ndjiki-nya | 3 | 4 | 0.44 |