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Proceedings Paper

Image processing: a neural network approach to 2-D Kalman filtering
Author(s): Roman W. Swiniarski; Michael P. Butler
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Paper Abstract

This paper describes an application of recurrent neural networks with feedback to the restoration of gray scale images corrupted by Gaussian disturbances. The two dimensional autoregressive (discrete homogeneous random Gaussian-Markov field) model of gray scale images are considered and identified as a base for future restoration. For the image restoration the concept of 2-D Kalman filtering (with reduced update procedure) has been utilized. The 2-D Kalman filter for the image restoration has been implemented as a tandem of two recurrent neural networks trained according to the 2-D Kalman filtering algorithm.

Paper Details

Date Published: 27 December 1990
PDF: 12 pages
Proc. SPIE 1347, Optical Information Processing Systems and Architectures II, (27 December 1990); doi: 10.1117/12.23399
Show Author Affiliations
Roman W. Swiniarski, San Diego State Univ. (United States)
Michael P. Butler, San Diego State Univ. (United States)

Published in SPIE Proceedings Vol. 1347:
Optical Information Processing Systems and Architectures II
Bahram Javidi, Editor(s)

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