Wavelet-based deconvolution for ill-conditioned systems

Ramesh Neelamani, Hyeokho Choi, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingChapter

27 Scopus citations

Abstract

ln this paper, we propose a new approach to wavelet-based deconvolution. Roughly speaking, the algorithm comprises Fourier-domain system inversion followed by wavelet-domain noise suppression. Our approach subsumes a number of other wavelet-based deconvolution methods. In contrast to other wavelet-based approaches, however, we employ a regularized inverse filter, which allows the algorithm to operate even when the inverse system is ill-conditioned or non-invertible. Using a mean-square-error metric, we strike an optimal balance between Fourier-domain and wavelet-domain regularization. The result is a fast deconvolution algorithm ideally suited to signals and images with edges and other singularities. In simulations with real data, the algorithm outperforms the LTI Wiener filter and other wavelet-based deconvolution algorithms in terms of both visual quality and MSE performance.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3241-3244
Number of pages4
Volume6
StatePublished - 1999
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: Mar 15 1999Mar 19 1999

Other

OtherProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99)
CityPhoenix, AZ, USA
Period3/15/993/19/99

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics

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