A full-plane block Kalman filter for image restoration

A two-dimensional method which uses a full-plane image model to generate a more accurate filtered estimate of an image that has been corrupted by additive noise and full-plane blur is presented. Causality is maintained within the filtering process by using multiple concurrent block estimators. In addition, true state dynamics are preserved, resulting in an accurate Kalman gain matrix. Simulation results on a test image corrupted by additive white Gaussian noise are presented for various image models and compared to those of the previous block Kalman filtering methods.<
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