Name
Abstract | ||
|---|---|---|
Image inpainting refers to restoring a damaged image with missing information. In recent years, there have been many developments on computational approaches to image inpainting problem [2, 4, 6, 9, 11---13, 27, 28]. While there are many effective algorithms available, there is still a lack of theoretical understanding on under what conditions these algorithms work well. In this paper, we take a step in this direction. We investigate an error bound for inpainting methods, by considering different image spaces such as smooth images, piecewise constant images and a particular kind of piecewise continuous images. Numerical results are presented to validate the theoretical error bounds. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/s10851-006-6865-7 | Journal of Mathematical Imaging and Vision |
Keywords | Field | DocType |
inpainting,total variation minimization,error analysis,inpainting domain,image restoration | Computer vision,Mathematical optimization,Inpainting,Total variation minimization,Artificial intelligence,Image restoration,Mathematics,Piecewise | Journal |
Volume | Issue | ISSN |
26 | 1-2 | 0924-9907 |
Citations | PageRank | References |
24 | 1.26 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tony F. Chan | 1 | 8733 | 659.77 |
| Sung Ha Kang | 2 | 430 | 29.39 |