Iterative Identification and Restoration of Images
Reginald L. Lagendijk, Aggelos K. Katsaggelos, Jan Biemond · 1991
The blur identification problem is formulated as a constrained maximum-likelihood problem. The constraints directly incorporate a priori known relations between the blur (and image model) coefficients, such as symmetry properties, into the identification procedure. The resulting nonlinear minimization problem is solved iteratively, yielding a very general identification algorithm. An example of blur identification using synthetic data is given.>