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

Blur idenfication is a fundamental problem in image restoration. We propose a new formulation for maximum likelihood (ML) blur identification based on parametric modeling of the blur in the continuous spatial coordinates. Unlike previous ML blur identification methods that have been based on discrete spatial domain blur models, our formulation makes it possible to find the ML estimate of the extent, as well as other parameters, of arbitrary point spread functions (PSF's) that admit a closed form parametric description in the continuous coordinates. Experimental results are presented for the cases of 1-D uniform motion blur, 2-D out-of-focus blur, and 2-D truncated Gaussian blur at different signal-to-noise ratios.