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Proceedings Paper

Restoration Of Noisy Images With Adaptive Windowing And Nonlinear Filtering
Author(s): Woo-Jin Song; William A. Pearlman
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Paper Abstract

A general problem with statistically-based estimators for images degraded by additive noise is their dependence on average quatities when image intensities vary rapidly and widely. The Wiener estimator, for example, given the stationary power spectrum on the object image and the noise, is known to produce a noisy effect in the flat intensity regions and a blurring or fuzzy effect in the edge regions on the restored image. The power spectra are usually estimated over regions containing both edges and flat regions and therefore are not truly representative of either regional type. In this work, we accept a nonstationary image model and utilize a novel adaptive windowing technique in conjunction with a nonlinear estimator to overcome the cited defects of other estimators. This technique is applied successively to simulated noisy one-dimensional feature waveforms, an arbitrarily selected noisy image scan line, noisy images with one-dimensional windowing, and noisy images with two-dimensional windowing. In each case, features of the edge and flat regions both are faithfully reconstructed. In fact, the restored images are remarkably sharp and clean. They appear far superior to the comparable Wiener restorations despite the fact that their mean-squared error is about the same or slightly larger.

Paper Details

Date Published: 20 November 1986
PDF: 9 pages
Proc. SPIE 0707, Visual Communications and Image Processing, (20 November 1986); doi: 10.1117/12.937267
Show Author Affiliations
Woo-Jin Song, Rensselaer Polytechnic Institute (United States)
William A. Pearlman, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 0707:
Visual Communications and Image Processing
T. Russell Hsing, Editor(s)

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