Signal enhancement in noise- and clutter-corrupted images using adaptive predictive filtering techniques

Tarun Soni; James R. Zeidler; Bhaskar D. Rao; Walter H. Ku

doi:10.1117/12.49789

1 December 1991 Signal enhancement in noise- and clutter-corrupted images using adaptive predictive filtering techniques

Tarun Soni, James R. Zeidler, Bhaskar D. Rao, Walter H. Ku

Author Affiliations +

Proceedings Volume 1565, Adaptive Signal Processing; (1991) https://doi.org/10.1117/12.49789
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

The recovery of an original image from its corrupted version is of importance in a number of applications. The detection of small and dim targets is one such problem, requiring the enhancement of target signals and suppression of noise and clutter in the image. Conventional methods like matched filtering require a priori knowledge of the target intensity spread function, the clutter correlation characteristics, etc. These techniques are difficult to implement if the image is nonstationary. This paper describes an adaptive clutter whitening technique which increases signal detectability in colored noise and clutter. Signal enhancement is based on the intrinsic differences in the spatial extent of the target relative to the clutter. An adaptive spatial filter is used to whiten the clutter present in the image. The output of such an adaptive spatial filter, termed the adaptive clutter whitener (ACW), is then passed on to a matched filter based detector. The receiver operating characteristics are found using Monte- Carlo simulation techniques, both for the ACW augmented matched filter detector and a conventional matched filter detector. It is seen that for highly correlated clutter, the ACW augmented detector has a better ROC than one without a prewhitening filter.

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Tarun Soni, James R. Zeidler, Bhaskar D. Rao, and Walter H. Ku "Signal enhancement in noise- and clutter-corrupted images using adaptive predictive filtering techniques", Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); https://doi.org/10.1117/12.49789

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