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Proceedings Paper

Efficient MRF image-restoration technique using deterministic scale-based optimization
Author(s): Murali M. Menon
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Paper Abstract

A method for performing piecewise smooth restorations on images corrupted with high levels of noise has been developed. Based on a Markov Random Field (MRF) model, the method uses a neural network sigmoid nonlinearity between pixels in the image to produce a restoration with sharp boundaries while providing noise reduction. The model equations are solved using the Gradient Descent Gain Annealing (GDGA) method (an efficient deterministic search algorithm) that typically requires less than 200 iterations for image restoration when implemented as a digital computer simulation. A novel feature of the GDGA method is that it automatically develops an annealing schedule by adaptively selecting the scale step size during iteration. The algorithm is able to restore images that have up to 71% of their pixels corrupted with non-Gaussian sensor noise. Results from simulations indicate that the MRF-based restoration remains useful at signal-to-noise ratios 5 to 6 dB lower than with the more commonly used median-filtering technique. These results are among the first such quantitative results in the literature.

Paper Details

Date Published: 22 March 1996
PDF: 15 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235965
Show Author Affiliations
Murali M. Menon, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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