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Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 45, Iss. 12 — Apr. 20, 2006
  • pp: 2697–2706

Improved optimization of soft-partition-weighted-sum filters and their application to image restoration

Yong Lin, Russell C. Hardie, Qin Sheng, Min Shao, and Kenneth E. Barner  »View Author Affiliations

Applied Optics, Vol. 45, Issue 12, pp. 2697-2706 (2006)

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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

OCIS Codes
(100.2980) Image processing : Image enhancement

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

Yong Lin, Russell C. Hardie, Qin Sheng, Min Shao, and Kenneth E. Barner, "Improved optimization of soft-partition-weighted-sum filters and their application to image restoration," Appl. Opt. 45, 2697-2706 (2006)

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