Wavelet-based Deconvolution for Ill-conditioned Systems

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)en_US
dc.citation.firstpage3241en_US
dc.citation.lastpage3244en_US
dc.citation.locationPhoenix, AZen_US
dc.citation.volumeNumber6en_US
dc.contributor.authorNeelamani, Rameshen_US
dc.contributor.authorChoi, Hyeokhoen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:55:12Zen_US
dc.date.available2007-10-31T00:55:12Zen_US
dc.date.issued1999-03-01en_US
dc.date.modified2006-06-21en_US
dc.date.note2001-08-27en_US
dc.date.submitted1999-03-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractIn 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.en_US
dc.description.sponsorshipTexas Instrumentsen_US
dc.description.sponsorshipDefense Advanced Research Projects Agencyen_US
dc.description.sponsorshipNational Science Foundationen_US
dc.identifier.citationR. Neelamani, H. Choi and R. G. Baraniuk, "Wavelet-based Deconvolution for Ill-conditioned Systems," vol. 6, 1999.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.1999.757532en_US
dc.identifier.urihttps://hdl.handle.net/1911/20135en_US
dc.language.isoengen_US
dc.subjectwavelet-based deconvolutionen_US
dc.subjectFourier-domain systemen_US
dc.subjectLTI Wiener filteren_US
dc.subjectMSE performanceen_US
dc.subjectwavelet-domain regularizationen_US
dc.subject.keywordwavelet-based deconvolutionen_US
dc.subject.keywordFourier-domain systemen_US
dc.subject.keywordLTI Wiener filteren_US
dc.subject.keywordMSE performanceen_US
dc.subject.keywordwavelet-domain regularizationen_US
dc.subject.otherImage Processing and Pattern analysisen_US
dc.subject.otherWavelet based Signal/Image Processingen_US
dc.subject.otherMultiscale Methodsen_US
dc.titleWavelet-based Deconvolution for Ill-conditioned Systemsen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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