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

Authors
  • Pavlovic, G
  • Tekalp, A M
Type
Published Article
Journal
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Publication Date
Jan 01, 1992
Volume
1
Issue
4
Pages
496–504
Identifiers
PMID: 18296182
Source
Medline
License
Unknown

Abstract

A formulation for maximum-likelihood (ML) blur identification based on parametric modeling of the blur in the continuous spatial coordinates is proposed. Unlike previous ML blur identification methods based on discrete spatial domain blur models, this formulation makes it possible to find the ML estimate of the extent, as well as other parameters, of arbitrary point spread functions 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.

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