The blur identification problem is formulated as a constrained maximum-likelihood problem. The constraints directly incorporate a piori 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.
|Original language||English (US)|
|Number of pages||4|
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|State||Published - 1988|
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering