Improved Optimization of Soft Partition Weighted Sum Filters and Their Application to Image Restoration
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.
OSA: The Optical Society
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.