Improved Optimization of Soft Partition Weighted Sum Filters and Their Application to Image Restoration

Author(s)

Yong Lin, University of Dayton
Russell C. Hardie, University of DaytonFollow
Qin Sheng, Baylor University
Kenneth E. Barner, University of Delaware

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