Semi-blind restoration from differently blurred visions of an image

Restoration of an object from K differently distorted versions in the presence of additive noise is considered. The point spread function (PSF) for each observation is unknown, however a noisy measurement of it is available. The blurring processes are assumed fixed and not random. The regression, the maximum likelihood, and the Wiener filters are derived. The consistency characteristics and the computation instabilities of these filters are discussed. Experimental results comparing the performance of these filters are presented