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Summary: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 8, NO. 4, APRIL 1999 589
Neural Network Decision Directed Edge-Adaptive
Kalman Filter for Image Estimation
Mahmood R. Azimi-Sadjadi, Rongrui Xiao, and Xi Yu
Abstract--A neural network-based scheme for decision directed edge-
adaptive Kalman filtering is introduced in this work. A backpropagation
neural network makes the decisions about the orientation of the edges
based on the information in a window centered at the current pixel being
processed. Then based upon the neural network output an appropriate
image model which closely matches the local statistics of the image is
chosen for the Kalman filter. This prevents the oversmoothing of the
edges, which would have otherwise been caused by the standard Kalman
filter. Simulation results are presented which show the effectiveness of
the proposed scheme.
Index Terms--Image restoration, Kalman filtering, neural networks.
I. INTRODUCTION
Adaptive Kalman filtering schemes which use spatial-varying im-
age models [1][3] take into account the local statistical information
within a processing window and thus preserve edges with a greater
noise reduction in nonedge regions. This obviously leads to a pro-
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