On the relationship between blind deconvolution and blind source separation

We explore the relationships between the two related tasks of blind deconvolution and blind source separation. The maximum likelihood method for parameter estimation is shown to provide a unifying framework for deriving blind deconvolution and blind source separation algorithms. To illustrate the structural relationships between the two tasks, we consider the problem of blind source separation under circulant mixing conditions, derive iterative algorithms for its solution, and then relate these algorithms to recently-proposed for blind deconvolution techniques. The results of these various studies suggests the potential benefits that can be obtained from the cross-fertilization of these two fields.