Name
Abstract | ||
|---|---|---|
We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33783-3_25 | ECCV (6) |
Keywords | Field | DocType |
general sparse image prior,general procedure,bayesian inference,blind image deconvolution,bayesian blind deconvolution,image modeling,proposed inference,unknown image,super-gaussian sparse image prior,inference method,natural image,general method | Algorithm design,Bayesian inference,Blind deconvolution,Pattern recognition,Computer science,Inference,Deconvolution,Sparse image,Artificial intelligence,Prior probability,Bayesian probability | Conference |
Volume | ISSN | Citations |
7577 | 0302-9743 | 60 |
PageRank | References | Authors |
1.41 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Derin Babacan | 1 | 534 | 26.60 |
| Rafael Molina | 2 | 1439 | 103.16 |
| Minh N. Do | 3 | 1681 | 133.55 |
| Aggelos K. Katsaggelos | 4 | 3410 | 340.41 |