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A fast Kalman filter for images degraded by both blur and noise

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3 Author(s)
Biemond, J. ; Delft University of Technology, Delft, The Netherlands ; Rieske, J. ; Gerbrands, J.J.

In this paper a fast Kalman filter is derived for the nearly optimal recursive restoration of images degraded in a deterministic way by blur and in a stochastic way by additive white noise. Straightforwardly implemented optimal restoration schemes for two-dimensional images degraded by both blur and noise create dimensionality problems which, in turn, lead to large storage and computational requirements. When the band-Toeplitz structure of the model matrices and of the distortion matrices in the matrix-vector formulations of the original image and of the noisy blurred observation are approximated by circulant matrices, these matrices can be diagonalized by means of the FFT. Consequently, a parallel set of N dynamical models suitable for the derivation of N low-order vector Kalman filters in the transform domain is obtained. In this way, the number of computations is reduced from the order of O(N4) to that of O(N^{2} \log _{2} N) for N × N images.

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Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:31 ,  Issue: 5 )