An optimal set-theoretic blind deconvolution scheme based on hybrid steepest descent method

In this paper, we propose a simple set theoretic blind deconvolution scheme based on a recently developed convex projection technique called Hybrid Steepest Descent Methods. The scheme is essentially motivated by Kundur and Hatzinakos’s idea that minimizes a certain cost function uniformly reflecting all a priori information such that (i) nonnegativity of the true image and (ii) support size of the original object. The most remarkable feature of the proposed scheme is that the proposed one can utilize each (II priors information separately from other ones, where some partial informations are treated in a set theoretic sense while the others are incorporated in a cost function to be minimized.