Image processing: a neural network approach to 2-D Kalman filtering
Roman W. Świniarski, Michael P. Butler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
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.