title: Improved optimization of soft-partition-weighted-sum filters and their application to image restoration.
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.
authors.affiliation: University of Dayton, Ohio 45469-0226, USA. yong.lin@ieee.org
authors.forename: Yong
authors.initials: Y
authors.lastname: Lin
authors.forename: Russell C
authors.initials: RC
authors.lastname: Hardie
authors.forename: Qin
authors.initials: Q
authors.lastname: Sheng
authors.forename: Min
authors.initials: M
authors.lastname: Shao
authors.forename: Kenneth E
authors.initials: KE
authors.lastname: Barner
mesh_terms: D000465:Algorithms; D005374:Filtration / Q000379:methods; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted