Optimal Sparse Representations for Blind Deconvolution of Images

摘要

The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used in modelling the log probability density function, which is suitable for sparse sources. We propose a method of sparsifica-tion, which allows blind deconvolution of sources with arbitrary distribution, and show how to find optimal sparsifying transformations by training.