Blind deconvolution of images using optimal sparse representations
Michael M. Bronstein, Alex M. Bronstein, Michael Zibulevsky, Y.Y. Zeevi · IEEE Transactions on Image Processing · 2005
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 as the nonlinear term for sparse sources. In addition, we propose a method of sparsification, which allows blind deconvolution of arbitrary sources, and show how to find optimal sparsifying transformations by supervised learning.