Identification of image and blur parameters for the restoration of noncausal blurs
| Autor: | Howard Kaufman, John W. Woods, A.M. Tekalp |
|---|---|
| Rok vydání: | 1986 |
| Předmět: |
business.industry
Estimation theory Matched filter ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Statistical parameter System identification Pattern recognition Image processing Kalman filter Iterative reconstruction Maximum likelihood sequence estimation Gradient noise symbols.namesake Gaussian noise Computer Science::Computer Vision and Pattern Recognition Signal Processing Image noise symbols Median filter Autoregressive–moving-average model Artificial intelligence business Image restoration Mathematics |
| Zdroj: | ICASSP Scopus-Elsevier |
| ISSN: | 0096-3518 |
| Popis: | An optimal statistical parameter estimation technique is presented for the identification of unknown image and blur model parameters. The development leads to an autoregressive moving average (ARMA) model identification problem, where the image model coefficients define the AR part, and the blur parameters define the MA part. Conditional maximum-likelihood estimates of the unknown parameters are derived both in the absence and in the presence of observation noise. The proposed algorithms constitute a generalization of previous work on blur identification in that they are able to locate the zero loci of the blurred image spectrum on the entire z 1 - z 2 plane. Simulation results, as well as photographically blurred images processed with the proposed algorithms, are shown as examples. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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