Title :
Fast Rauch-Tung-Striebel smoother-based image restoration for noncausal images
Author_Institution :
Dept. of Comput. Sci., York Univ., North York, Ont., Canada
fDate :
3/1/2004 12:00:00 AM
Abstract :
We describe a technique for restoration of blurred images corrupted with additive noise. Our algorithm uses a practical implementation of the Rauch-Tung-Striebel (RTS) smoother-based on noncausal prediction that models the blurred image as a finite-lattice Gauss-Markov random process (GMRP). The one-sided regressors of the GMRP converge at a geometric rate to shift-invariant values along the rows of the image. This leads to a steady-state solution for the RTS filter. Experimental results illustrate the superiority of our RTS-based algorithm over Wiener filter, deterministic filters, and filters that use the one-sided causal state model.
Keywords :
Gaussian processes; Kalman filters; Markov processes; image restoration; noise; random processes; Kalman-Bucy filter; additive noise; fast Rauch-Tung-Striebel smoother; finite-lattice Gauss-Markov random process; geometric rate; image restoration; noncausal images; noncausal prediction; one-sided regressors; random process; shift-invariant values; steady-state solution; Additive noise; Covariance matrix; Degradation; Gaussian processes; Image restoration; Predictive models; Random processes; Signal processing algorithms; Steady-state; Wiener filter;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2003.822922