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Article
Improved Optimization of Soft Partition Weighted Sum Filters and Their Application to Image Restoration
Applied Optics
  • Yong Lin, University of Dayton
  • Russell C. Hardie, University of Dayton
  • Qin Sheng, Baylor University
  • Kenneth E. Barner, University of Delaware
Document Type
Article
Publication Date
4-1-2006
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
Peer Reviewed
Yes
Citation Information
Yong Lin, Russell C. Hardie, Qin Sheng and Kenneth E. Barner. "Improved Optimization of Soft Partition Weighted Sum Filters and Their Application to Image Restoration" Applied Optics Vol. 45 Iss. 12 (2006)
Available at: http://works.bepress.com/russell_hardie/34/