Iterative Wiener filters for image restoration

Abstract

Wiener filtering, an iterative procedure which successively uses the Wiener-filtered signal as an improved prototype to update the covariance estimates, is investigated. The convergent properties of this iterative Wiener filter are analyzed. It has been shown that the iterative Wiener filter converges to a fixed point which does not correspond to the true covariance. Based on the analysis presented, and iterative filter is proposed to correct for the convergence error which results in suboptimal performance of the original iterative Wiener filter. The performance of this filter is shown to be theoretically optimal. Experiments are conducted in a practical setting to demonstrate the effectiveness of the proposed methods. >