Optimizing deconvolution techniques by the application of the Münchhausen meta algorithm.

A deconvolution applied to disturbed data often gives poor results, due to fundamental difficulties associated with ill-posed problems. Many numerical and theoretical methods have been invented to circumvent this phenomenon. Their performance varies, depending on the given problem and data. The main aim of this paper is to provide a decision rule for choosing a method for deconvolution and application of this method to the same data. We have called this meta-algorithm Münchhausen. In this paper we introduce and describe for the first time the basic principle of artificial disturbance of the data in the set-up of deconvolution. We demonstrate some interesting features of the random procedure Münchhausen, such as the non parametric set-up, robustness to disturbance of the data and last but not least good performance.