An experimental study of the effects of noise on a class of iterative deconvolution algorithms

There has recently been considerable interest in iterative deconvolution algorithms. Some of these algorithms seek to improve performance by incorporating á priori knowledge of the components of a convolution. For example, incorporation of both positivity and finite interval constraints tends to automatically introduce an extrapolation of the bandwidth. A question which naturally arises concerns the effect of noise on the performance of such algorithms. In this paper we describe an experimental evaluation of the effect of noise using synthetic data. Results of extensive tests are presented and some suggestions for mitigating the effects of noise are offered.