A very fast Kalman filter for image restoration

Author

Zhang, Jin Yun ; Steenaart, Willem

Author_Institution

Dept. of Electr. Eng., Ottawa Univ., Ont., Canada

fYear

1990

fDate

1-3 May 1990

Firstpage

250

Abstract

The application of 2-D Kalman filtering to the restoration of images degraded by linear space invariant blur and additive white Gaussian noise is described. R.P. Roesser´s 2-D local state space model (1975) is used to represent the image process and the blur process. As a result, a simple procedure for establishing the Kalman filter equations is obtained. This scalar filtering algorithm provides a computationally feasible procedure for the restoration of large images. To speed up the Kalman filtering procedure, a VLSI systolic array structure is presented. For higher speed and higher utilization of this processor, a diagonal scanning method is suggested. The filter scheme can be easily extended to the causal image model and the causal blur model with nonsymmetric half-plane support

Keywords

Kalman filters; filtering and prediction theory; picture processing; state-space methods; systolic arrays; white noise; 2-D Kalman filtering; 2-D local state space model; VLSI systolic array structure; additive white Gaussian noise; causal blur model; causal image model; diagonal scanning method; fast Kalman filter; image restoration; linear space invariant blur; nonsymmetric half-plane support; scalar filtering algorithm; Additive white noise; Degradation; Equations; Filtering algorithms; Image restoration; Kalman filters; Nonlinear filters; State-space methods; Systolic arrays; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1990., IEEE International Symposium on

Conference_Location

New Orleans, LA

Type

conf

DOI

10.1109/ISCAS.1990.111999

Filename

111999