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

We propose a simple set-theoretic blind deconvolution scheme based on a previously developed convex projection technique called hybrid steepest descent methods. The scheme is essentially motivated by Kundur and Hatzinakos´s (see IEEE Signal Processing Magazine, vol.13, no.3, p.43-63, 1996 and IEEE Trans. Signal Processing, vol.46, p.375-90, 1998) idea that minimizes a certain cost function uniformly reflecting all a priori information such as the (i) nonnegativity of the true image and the (ii) support size of the original object. The most remarkable feature of the proposed scheme is that one can utilize each a priori information separately from other ones, where some partial information are treated in a set theoretic sense while the others are incorporated in a cost function to be minimized