PRIOR KNOWLEDGE AND MULTISCALING IN STATISTICAL ESTIMATION OF SIGNAL-TO-NOISE RATIO - APPLICATION TO DECONVOLUTION REGULARIZATION

An improvement to the choice of the regularization parameter involved in a deconvolution procedure is proposed, It is based on a statistical model allowing a good estimation of the spectral signal-to-noise rati o. To this aim, first, a separation of signal and noise is performed t hrough a multiresolution scheme, from the variance behavior of the wav elet coefficients of data as a function of resolution. Second, based o n this separation, the autocorrelation functions of the signal and of the noise, and hence the spectral signal-to-noise ratio, are calculate d with a probabilistic model incorporating the prior knowledge about t he underlying physical phenomenon, This model is illustrated with a 1D example.