Image processing: a neural network approach to 2-D Kalman filtering

Roman W. Swiniarski; Michael P. Butler

doi:10.1117/12.23399

27 December 1990 Image processing: a neural network approach to 2-D Kalman filtering

Roman W. Swiniarski, Michael P. Butler

Proceedings Volume 1347, Optical Information Processing Systems and Architectures II; (1990) https://doi.org/10.1117/12.23399
Event: 34th Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, 1990, San Diego, CA, United States

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.

Citation Download Citation

Roman W. Swiniarski and Michael P. Butler "Image processing: a neural network approach to 2-D Kalman filtering", Proc. SPIE 1347, Optical Information Processing Systems and Architectures II, (27 December 1990); https://doi.org/10.1117/12.23399

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