Title

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

Issue

9

Peer Reviewed

yes