Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
