Wavelet-based deconvolution for ill-conditioned systems

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dc.citation.bibtexNamearticleen_US
dc.citation.journalTitleIEEE Transactions on Image Processingen_US
dc.contributor.authorNeelamani, Rameshen_US
dc.contributor.authorChoi, Hyeokhoen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:55:25Zen_US
dc.date.available2007-10-31T00:55:25Zen_US
dc.date.issued2000-02-01en_US
dc.date.modified2004-11-07en_US
dc.date.submitted2004-11-06en_US
dc.descriptionJournal Paperen_US
dc.description.abstractWe propose a hybrid approach to wavelet-based deconvolution that comprises Fourier-domain system inversion followed by wavelet-domain noise suppression. In contrast to other wavelet-based deconvolution approaches, the algorithm employs a <i>regularized inverse filter</i>, which allows it to operate even when the system is non-invertible. Using a mean-square-error (MSE) metric, we strike an optimal balance between Fourier-domain regularization (matched to the convolution operator) and wavelet-domain regularization (matched to the signal/image). Theoretical analysis reveals that the optimal balance is determined by the Fourier-domain operator structure and the economics of the wavelet-domain signal representation. The resulting algorithm is fast (<i>O(N\log N)</i> complexity for signals/images of <i>N</i> samples) and is well-suited to data with spatially-localized phenomena such as edges and ridges. In addition to enjoying asymptotically optimal rates of error decay for certain systems, the algorithm also achieves excellent performance at fixed data lengths. In real data experiments, the algorithm outperforms the conventional time-invariant Wiener filter and other wavelet-based image restoration algorithms in terms of both MSE performance and visual quality.en_US
dc.identifier.citationR. Neelamani, H. Choi and R. G. Baraniuk, "Wavelet-based deconvolution for ill-conditioned systems," <i>IEEE Transactions on Image Processing,</i> 2000.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.1999.757532en_US
dc.identifier.urihttps://hdl.handle.net/1911/20139en_US
dc.language.isoengen_US
dc.relation.softwarehttp://www.dsp.rice.edu/software/WaRD/en_US
dc.subjectTemporaryen_US
dc.subject.keywordTemporaryen_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.typeJournal articleen_US
dc.type.dcmiTexten_US
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