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 between a model family of probability distributions defined using the linear image degradation model and a desired family of probability distributions constrained to be concentrated on the observed data. 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 |
|---|---|---|
2011 | 10.1109/TIP.2011.2105881 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
image restoration,maximum likelihood estimation,statistical distributions,Kullback-Leibler divergence approach,additive noise,blurring process,linear image degradation model,maximum-likelihood blind image restoration,multivariate Gaussian processes,point spread function,probability distributions,unknown covariance matrices,Blind image restoration,Kullback–Leibler information,maximum-likelihood estimation | Pattern recognition,Image processing,Multivariate normal distribution,Probability distribution,Artificial intelligence,Gaussian process,Covariance matrix,Image restoration,Mathematics,Kullback–Leibler divergence,Covariance | Journal |
Volume | Issue | ISSN |
20 | 7 | 1057-7149 |
Citations | PageRank | References |
9 | 0.51 | 15 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abd-Krim Seghouane | 1 | 78 | 12.27 |