Quasi Maximum Likelihood Blind Deconvolution of Images Using Optimal Sparse Representations
Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, Yehoshua Y. Josh Zeevi · 2003
A quasi maximum likelihood framework for blind deconvolution of images is presented. We generalize the relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of time signals, and provide asymptotic analysis of its performance. Smooth approximation of the absolute value is used to model the log probability density function, which is suitable for sparse sources. In addition, we propose a method of sparsification, which allows to perform blind deconvolution of sources with arbitrary distribution, and show how to find optimal sparsifying transformations by training.