Maximum likelihood parametric blur identification based on a continuous spatial domain model

A new formulation is proposed for maximum likelihood (ML) blur identification that is based on a parametric description of the blur in the continuous spatial coordinates. The aim of this formulation is to find the ML estimate of the extent of certain point spread functions (PSF). It is shown that this can be achieved by formulating the problem in the continuous spatial coordinates, as opposed to using the conventional discrete spatial domain model. Experimental results are presented for the cases of uniform motion blur, out of focus blur and truncated Gaussian blur at various signal-to-noise ratios.