Recurrent neural network application to image filtering: 2-D Kalman filtering approach

Roman W. Swiniarski; Andrzej Dzielinski; Slawomir Skoneczny; Michael P. Butler

doi:10.1117/12.44329

1 April 1991 Recurrent neural network application to image filtering: 2-D Kalman filtering approach

Roman W. Swiniarski, Andrzej Dzielinski, Slawomir Skoneczny, Michael P. Butler

Proceedings Volume 1451, Nonlinear Image Processing II; (1991) https://doi.org/10.1117/12.44329
Event: Electronic Imaging '91, 1991, San Jose, CA, United States

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.

Citation Download Citation

Roman W. Swiniarski, Andrzej Dzielinski, Slawomir Skoneczny, and Michael P. Butler "Recurrent neural network application to image filtering: 2-D Kalman filtering approach", Proc. SPIE 1451, Nonlinear Image Processing II, (1 April 1991); https://doi.org/10.1117/12.44329

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