Improved Optimization of Soft Partition Weighted Sum Filters and Their Application to Image Restoration
Document Type
Article
Publication Date
4-2006
Publication Source
Applied Optics
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.
Inclusive pages
2697-2706
ISBN/ISSN
1559-128X
Publisher
OSA: The Optical Society
Volume
45
Peer Reviewed
yes
Issue
12
eCommons Citation
Lin, Yong; Hardie, Russell C.; Sheng, Qin; and Barner, Kenneth E., "Improved Optimization of Soft Partition Weighted Sum Filters and Their Application to Image Restoration" (2006). Electrical and Computer Engineering Faculty Publications. 74.
https://ecommons.udayton.edu/ece_fac_pub/74
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