Name
Abstract | ||
|---|---|---|
A new algorithm for Maximum likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5650975 | Image Processing |
Keywords | Field | DocType |
Gaussian processes,blind source separation,covariance matrices,image restoration,iterative methods,maximum likelihood estimation,minimisation,optical transfer function,Kullback-Leibler divergence,additive noise,alternating minimization,covariance matrices,iterative method,maximum likelihood blind image restoration,multivariate Gaussian process,point spread function,Blind image restoration,Kullback-Leibler information | Noise measurement,Pattern recognition,Computer science,Multivariate normal distribution,Gaussian process,Artificial intelligence,Covariance matrix,Image restoration,Blind signal separation,Kullback–Leibler divergence,Covariance | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 5 |
PageRank | References | Authors |
0.47 | 4 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abd-Krim Seghouane | 1 | 78 | 12.27 |