Joint MAP estimation for blind deconvolution

Abstract

Blind deconvolution aims at reconstructing an image from its blurred and noisy version, when the blur kernel is not known. It has been acknowledged that the naive maximum aposteriori probability (MAP) algorithm favors a no-blur solution [3]. In [8] the failure of the direct MAP approach is addressed and it is proved that a simultaneous MAP estimation of the image and the point spread function (PSF) fails, providing a trivial solution. In contrast, we show that an appropriate choice of PSF prior during joint MAP estimation does provide a non-trivial solution. We provide the feasible range for the PSF regularization factor which would prevent a trivial solution.