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|Title:||Parametric modeling of blurred images for image restoration||Authors:||Premaratne, P.
|Issue Date:||2000||Citation:||Premaratne, P.,Ko, C.C. (2000). Parametric modeling of blurred images for image restoration. Conference Record of the Asilomar Conference on Signals, Systems and Computers 2 : 1727-1730. ScholarBank@NUS Repository.||Abstract:||Almost all of parameter estimation schemes for image restoration to date, attempt to model the true image as a autoregressive model and the point spread function as a moving average model and assume the symmetry of the point spread function in order to reduce the computational complexity. Autoregressive process builds the true image by passing a Gaussian white noise process through a filter and may result in unstable systems and optimization of parameters could be trapped in local minima. In this article a different approach is presented with simulation results where initial white Gaussian process is replaced by scaled degraded image avoiding optimization problems.||Source Title:||Conference Record of the Asilomar Conference on Signals, Systems and Computers||URI:||http://scholarbank.nus.edu.sg/handle/10635/72845||ISSN:||10586393|
|Appears in Collections:||Staff Publications|
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