Discretization of image restoration problems often leads to a discrete inverse ill posed problem: the discretized operator is so badly conditioned that it can be actually considered as undetermined. In this case one should single out the solution which is the nearest to the desired solution.The usual way to do it is to regularize the problem. In this paper we focus on the computational aspects of the Wiener filter, introduced at the beginning of the 40th, i.e. about 20 years before the Tikhonov regularization, in the framework of the regularization methods. The emhpasis will be given on its reliability and its efficiency, both of which become more and more important as the size and the complexity of the real problem grow and the demand for advanced real-time processing increases.

The Wiener Filter and Regularization Methods for Image Restoration Problems

D'AMORE, LUISA;
1999

Abstract

Discretization of image restoration problems often leads to a discrete inverse ill posed problem: the discretized operator is so badly conditioned that it can be actually considered as undetermined. In this case one should single out the solution which is the nearest to the desired solution.The usual way to do it is to regularize the problem. In this paper we focus on the computational aspects of the Wiener filter, introduced at the beginning of the 40th, i.e. about 20 years before the Tikhonov regularization, in the framework of the regularization methods. The emhpasis will be given on its reliability and its efficiency, both of which become more and more important as the size and the complexity of the real problem grow and the demand for advanced real-time processing increases.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/184016
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