Abstract
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) |
|---|---|
| Pages (from-to) | 992-995 |
| Number of pages | 4 |
| Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| State | Published - 1988 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering