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Proceedings Paper

Noise suppression and barrier crossing in Monte Carlo image-restoration method
Author(s): Abolfazl M. Amini
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Paper Abstract

In this paper, an efficient approach for image restoration of noisy data is suggested. This approach combines the Monte Carlo image restoration technique and the Morrison noise removal methods. The mean squared error (MSE) criterion is used to test the performance of the Monte Carlo method with and without prior-application of the Morrison noise removal method. The methods for facilitating the Monte Carlo walk to the brightest regions of the image are discussed and a new approach is suggested. It is shown that the Monte Carlo technique is potentially very fast with good resolution. The Morrison noise removal method smoothes the data at the first iteration and proceeds to restore the data back to its original noisy form at later iterations. To achieve some noise suppression, one can stop the Morrison iterations before it converges to the original noisy form. The Monte Carlo method is then applied to the noise suppressed data.

Paper Details

Date Published: 28 March 1995
PDF: 12 pages
Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); doi: 10.1117/12.205211
Show Author Affiliations
Abolfazl M. Amini, NASA Stennis Space Ctr. and Southern Univ. (United States)

Published in SPIE Proceedings Vol. 2424:
Nonlinear Image Processing VI
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham; Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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