Abstract

Soft-partition-weighted-sum (Soft-PWS) filters are a class of spatially adaptive moving-window filters for signal and image restoration. Their performance is shown to be promising. However, optimization of the Soft-PWS filters has received only limited attention. Earlier work focused on a stochastic-gradient method that is computationally prohibitive in many applications. We describe a novel radial basis function interpretation of the Soft-PWS filters and present an efficient optimization procedure. We apply the filters to the problem of noise reduction. The experimental results show that the Soft-PWS filter outperforms the standard partition-weighted-sum filter and the Wiener filter.

© 2006 Optical Society of America

» View Full Text: Acrobat PDF (1411 KB)

History
Original Manuscript: July 20, 2005
Manuscript Accepted: October 11, 2005

References

Please [login to View References]

Citing Articles

OSA is able to provide readers links to other articles that cite this paper by participating in CrossRef’s Forward Linking service. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Advanced Search