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 s...
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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 within the framework of the regularization methods. The emphasis is 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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