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Proceedings Paper

Neural network implementation of the SMSE filter for imaging processing
Author(s): Edwin P. K. Wong; Ling Guan; Stuart W. Perry
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Paper Abstract

This paper presents an implementation and enhancement of the SMSE (scaled mean square error) filter, using a Hopfield neural network based algorithm. We show the development of the original SMSE filter from the MMSE (minimum mean square error) filter and the PMSE (parametric mean square error) filter, both of which suffer from the oversmooth phenomena. The SMSE filter is more efficient than the PMSE filter in terms of noise removal as it does not take into account all the correlation factors used for image restoration. An adaptive SMSE filter is also presented. The adaptive SMSE filter uses a mask operation technique. A user- defined mask is moved across the image and the filtering parameters are computed based on the local image statistics of the region below the mask. The original and adaptive SMSE filters are implemented using a Hopfield neural network based algorithm. A number of experiments were performed to test the filter characteristics.

Paper Details

Date Published: 5 March 1996
PDF: 9 pages
Proc. SPIE 2661, Real-Time Imaging, (5 March 1996); doi: 10.1117/12.234640
Show Author Affiliations
Edwin P. K. Wong, Univ. of Sydney (Australia)
Ling Guan, Univ. of Sydney (Australia)
Stuart W. Perry, Univ. of Sydney (Australia)


Published in SPIE Proceedings Vol. 2661:
Real-Time Imaging
Phillip A. Laplante; Alexander D. Stoyenko; Divyendu Sinha, Editor(s)

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