Fast relative newton algorithm for blind deconvolution of images
Alex M. Bronstein, Michael Zibulevsky, Michael M. Bronstein, Y.Y. Zeevi · 2005
We present an efficient Newton-like algorithm for quasimaximum likelihood (QML) blind deconvolution of images. This algorithm exploits the sparse structure of the Hessian. An optimal distribution-shaping approach by means of sparsification allows one to use simple and convenient sparsity prior for processing of a wide range of natural images. Simulation results demonstrate the efficiency of the proposed method.