Subspace Partition Weighted Sum Filters for Image Restoration
Document Type
Article
Publication Date
8-2005
Publication Source
IEEE Signal Processing Letters
Abstract
The previously proposed partition-based weighted sum (PWS) filters combine vector quantization (VQ) and linear finite impulse response (FIR) Wiener filtering concepts. By partitioning the observation space and applying a tuned Wiener filter to each partition, the PWS is spatially adaptive and has been shown to perform well in noise reduction applications. In this letter, we propose the subspace PWS (SPWS) filter and evaluate the efficacy of the SPWS filter in image deconvolution and noise reduction applications. In the SPWS filter, we project the observation vectors into a subspace using principal component analysis (PCA), or other methods, prior to partitioning. This subspace projection can dramatically reduce the computational burden associated with partitioning, especially for large window sizes. In some cases, performance is also enhanced due to improved partitioning.
Inclusive pages
613 - 616
ISBN/ISSN
1070-9908
Publisher
IEEE: Institute of Electrical and Electronics Engineers
Volume
12
Peer Reviewed
yes
Issue
9
eCommons Citation
Lin, Yong; Hardie, Russell C.; and Barner, Kenneth E., "Subspace Partition Weighted Sum Filters for Image Restoration" (2005). Electrical and Computer Engineering Faculty Publications. 73.
https://ecommons.udayton.edu/ece_fac_pub/73
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