Bayesian filters for image estimation

We consider recursive estimation of images modeled by non-Gaussian autoregressive (AR) models and corrupted by spatially white Gaussian noise. The goal is to find a recursive algorithm to compute a near minimum mean squared error (MMSE) estimate of each scene pixel using a fixed lookahead of D rows...

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Bibliographic Details
Main Authors: Kadaba, S.R., Gelfand, S.B.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Computational modeling
Computer errors
Contracts
Filtering
Gaussian noise
Image restoration
Kalman filters
Layout
Recursive estimation
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Summary:We consider recursive estimation of images modeled by non-Gaussian autoregressive (AR) models and corrupted by spatially white Gaussian noise. The goal is to find a recursive algorithm to compute a near minimum mean squared error (MMSE) estimate of each scene pixel using a fixed lookahead of D rows and D columns of the observations. Our method is based on a simple approximation which facilitates the development of a useful suboptimal nonlinear estimator. In the process, we draw on the well-known reduced update Kalman filter to circumvent computational load problems. A simulation example demonstrates the non-Gaussian nature of the residual for an AR image model and that our algorithm compares favourably with Kalman filtering techniques in such cases.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1996.545871