Share Email Print

Proceedings Paper

A Minimum-Error, Minimum-Correlation Filter For Images
Author(s): Woo-Jin Song; William A. Pearlman
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We treat the linear estimation problem with two simultaneous, competing objectives: minimum mean-squared error and minimum error-signal correlation. The latter objective minimizes the signal component in the error and maximizes the correlation of the estimator with the signal. The problem is solved, both for the scalar and stationary random process cases, as an optimal trade-off which produces a slightly higher mean-squared error and a much larger reduction in error-signal correlation over that of the minimum mean-squared error single objective solution. The optimal trade-off solution, which we call the mini-mum-error, minimum correlation (MEMC) filter is then applied to the problem of recovering space-invariant, blurred images with additive noise. As the theory predicts, the images restored through the MEMC filters are sharper and clearer than their minimum mean-squared error (Wiener) filter counterparts, but slightly noisier in appearance. Most viewers prefer the MEMC restorations to the Wiener ones, despite the noisier appearance.

Paper Details

Date Published: 10 December 1986
PDF: 8 pages
Proc. SPIE 0697, Applications of Digital Image Processing IX, (10 December 1986); doi: 10.1117/12.976223
Show Author Affiliations
Woo-Jin Song, Rensselaer Polytechnic Institute (United States)
William A. Pearlman, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 0697:
Applications of Digital Image Processing IX
Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top