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dc.contributor.authorNeelamani, Ramesh
Choi, Hyeokho
Baraniuk, Richard G.
dc.creatorNeelamani, Ramesh
Choi, Hyeokho
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T00:55:09Z
dc.date.available 2007-10-31T00:55:09Z
dc.date.issued 1999-07-20
dc.date.submitted 1999-07-20
dc.identifier.citation R. Neelamani, H. Choi and R. G. Baraniuk, "Wavelet-Based Deconvolution Using Optimally Regularized Inversion for Ill-Conditioned Systems," vol. 3813, 1999.
dc.identifier.urihttps://hdl.handle.net/1911/20134
dc.description Conference Paper
dc.description.abstract We propose a hybrid approach to wavelet-based deconvolution that comprises Fourier-domain system inversion followed by wavelet-domain noise suppression. In contrast to conventional wavelet-based deconvolution approaches, the algorithm employs a {em {regularized inverse filter}}, 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 system) and wavelet-domain regularization (matched to the signal/image). Theoretical analysis reveals that the optimal balance is determined by the economics of the signal representation in the wavelet domain and the operator structure. The resulting algorithm is fast (O(Nlog_2^2N) complexity for signals/images of $N$ samples) and is well-suited to data with spatially-localized phenomena such as edges. In addition to enjoying asymptotically optimal rates of error decay for certain systems, the algorithm also achieves excellent performance at fixed data lengths. In simulations with real data, the algorithm outperforms the conventional time-invariant Wiener filter and other wavelet-based deconvolution algorithms in terms of both MSE performance and visual quality.
dc.description.sponsorship Texas Instruments
dc.description.sponsorship Defense Advanced Research Projects Agency
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.subjectdeconvolution
restoration
wavelets
regularization
dc.subject.otherImage Processing and Pattern analysis
Wavelet based Signal/Image Processing
Multiscale Methods
dc.title Wavelet-Based Deconvolution Using Optimally Regularized Inversion for Ill-Conditioned Systems
dc.type Conference paper
dc.date.note 2001-09-02
dc.citation.bibtexName inproceedings
dc.date.modified 2001-09-02
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keyworddeconvolution
restoration
wavelets
regularization
dc.citation.volumeNumber 3813
dc.citation.location Denver, CO
dc.citation.conferenceName SPIE International Conference on Optical Science, Engineering, and Instrumentation
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1117/12.366812
dc.citation.firstpage 58
dc.citation.lastpage 72


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    Publications by Rice Faculty and graduate students in digital signal processing.
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    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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