Image restoration in signal-dependent noise using a Markovian covariance model

An image distribution is modeled as a spatially nonstationary process having a Markovian covariance model. Images corrupted by signal-dependent noise are restored using maximization of the a posteriori probability density function as the processing criterion. The parameters of the signal distributio...

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Bibliographic Details
Published in:Computer vision, graphics, and image processing graphics, and image processing, 1984-01, Vol.28 (3), p.363-376
Main Authors: Kasturi, Rangachar, Walkup, John F., Krile, Thomas F.
Format: Article
Language:English
Subjects:
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Imaging and optical processing
Optics
Physics
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Summary:An image distribution is modeled as a spatially nonstationary process having a Markovian covariance model. Images corrupted by signal-dependent noise are restored using maximization of the a posteriori probability density function as the processing criterion. The parameters of the signal distribution required by the estimators are obtained using local spatial statistics. Results of computer simulations conducted to evaluate the performances of the estimators are presented.
ISSN:0734-189X
1557-895X
DOI:10.1016/S0734-189X(84)80014-6