Name
Abstract | ||
|---|---|---|
The authors describe a method for restoring sequences of noisy images obtained by acquiring different views of the same scene. The method uses a 3-D Markov random field and a least-square-error matching to establish the temporal-spatial neighborhood of a pixel in an image under restoration. The problem of image sequence restoration is posed as the problem of maximizing the conditional probabilities. This task is accomplished by a modified version of the iterated conditional modes method where Gibbs distribution is used to model the prior probability |
Year | DOI | Venue |
|---|---|---|
1989 | 10.1109/ICASSP.1989.266703 | Glasgow |
Keywords | DocType | ISSN |
markov processes,picture processing,3-d markov random fields,bayesian restoration,conditional probabilities,image restoration,image sequences,least-square-error matching,noisy images,temporal-spatial neighborhood,conditional probability,layout,pixel,degradation,gibbs distribution,bayesian methods,lattices | Conference | 1520-6149 |
Citations | PageRank | References |
3 | 0.68 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong, L. | 1 | 3 | 0.68 |
| Brzakovic, D. | 2 | 3 | 0.68 |