Name
Abstract | ||
|---|---|---|
This paper considers two approaches to perform image restoration while preserving the contrast. The first one is the Total Variation-based Bregman iterations while the second consists in the minimization of an energy that involves robust edge preserving regularization. We show that these two approaches can be derived form a common framework. This allows us to deduce new properties and to extend and generalize these two previous approaches. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5413353 | ICIP |
Keywords | Field | DocType |
image restoration,iterative methods,Bregmanized total variation,energy minimization,image restoration,robust edge preserving regularization,total variation-based Bregman iterations | Mathematical optimization,Noise measurement,Pattern recognition,Computer science,Iterative method,Algorithm,Regularization (mathematics),Total variation denoising,Minification,Artificial intelligence,Image restoration | Conference |
Citations | PageRank | References |
1 | 0.37 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérôme Darbon | 1 | 512 | 41.96 |
| Igor Ciril | 2 | 1 | 1.04 |
| Antonio Marquina | 3 | 431 | 45.30 |
| Tony F. Chan | 4 | 8733 | 659.77 |
| Stanley Osher | 5 | 7973 | 514.62 |