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22 March 1996 Efficient MRF image-restoration technique using deterministic scale-based optimization
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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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Murali M. Menon "Efficient MRF image-restoration technique using deterministic scale-based optimization", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996);

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