Title :
Constrained Kalman filtering for image restoration
Author_Institution :
Microtel Pacific Res., Burnaby, BC, Canada
Abstract :
The author considers the incorporation of deterministic a priori signal information in the Kalman filtering of images. This information, in the form of constraints, is used to achieve `ringing´ reduction by adaptive regularization of the restoration filter. The signal constraints are first transformed into constraints on the Kalman gain. Constrained optimization of the Kalman gain is then implemented using a penalty function approach. The constraints considered are bounds on signal amplitude and signal local variance. The proposed scheme provides a hybrid image restoration that minimizes the mean square error subject to the given a priori constraints. A simple but effective heuristic Kalman algorithm, which uses a `tuning´ parameter to achieve ringing reduction, is proposed
Keywords :
Kalman filters; filtering and prediction theory; picture processing; Kalman filtering; Kalman gain; adaptive regularization; constrained optimisation; deterministic a priori signal information; heuristic Kalman algorithm; image restoration; mean square error; penalty function; restoration filter; ringing reduction; signal amplitude; signal constraints; signal local variance; Adaptive filters; Constraint optimization; Humans; Image restoration; Information filtering; Information filters; Iterative algorithms; Kalman filters; Signal restoration; Stochastic resonance;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on
Conference_Location :
Glasgow
DOI :
10.1109/ICASSP.1989.266701