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Proceedings Paper

Recurrent neural network application to image filtering: 2-D Kalman filtering approach
Author(s): Roman W. Swiniarski; Andrzej Dzielinski; Slawomir Skoneczny; Michael P. Butler
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Paper Abstract

A Kalman filter for a class of 2D image state-space model is presented. Kalman filter equations are derived for the reduced version of 2D system model and resulting state estimate is expressed in terms of original 2D system. A neural network computing the Kalman filter gain has been designed. This way burdensome Riccati equation solution was improved. The evaluated Kalman filter gain is used to estimate the real input noisy image. As a result a restore image is obtained.

Paper Details

Date Published: 1 April 1991
PDF: 8 pages
Proc. SPIE 1451, Nonlinear Image Processing II, (1 April 1991); doi: 10.1117/12.44329
Show Author Affiliations
Roman W. Swiniarski, San Diego State Univ. (United States)
Andrzej Dzielinski, Warsaw Univ. of Technology (Poland)
Slawomir Skoneczny, Warsaw Univ. of Technology (Poland)
Michael P. Butler, San Diego State Univ. (United States)


Published in SPIE Proceedings Vol. 1451:
Nonlinear Image Processing II
Edward R. Dougherty; Gonzalo R. Arce; Charles G. Boncelet, Editor(s)

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