Identification and restoration using parallel Kalman filters
| Autor: | Jie Biemond, Howard Kaufman |
|---|---|
| Rok vydání: | 1987 |
| Předmět: |
business.industry
Filter (signal processing) Iterative reconstruction Kalman filter Set (abstract data type) Parameter identification problem Identification (information) Autoregressive model Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence business Algorithm Image restoration Mathematics |
| Zdroj: | 26th IEEE Conference on Decision and Control. |
| Popis: | In this paper a parallel identification and restoration procedure is described for images with symmetric, noncausal blurs. It is shown that the identification problem can be specified as a parallel set of one-dimensional complex autoregressive moving-average (ARMA) identification problems. By expressing the ARMA models as equivalent infinite-order autoregressive (AR) models, an entirely linear estimation procedure can be followed. It will be shown that under the condition of blur symmetry, it is possible to reconstruct a useful noncausal set of MA (blur) parameters from the identified minimum-phase set. The thus identified image model and blur parameters are supplied to a parallel Kalman restoration filter. Several identification and restoration results on image data are given as examples. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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