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dc.contributor.advisor Baraniuk, Richard G.
dc.creatorNeelamani, Ramesh
dc.date.accessioned 2009-06-04T08:39:53Z
dc.date.available 2009-06-04T08:39:53Z
dc.date.issued 1999
dc.identifier.urihttps://hdl.handle.net/1911/17290
dc.description.abstract This thesis proposes a new approach to wavelet-based image 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 regularized inverse filter, which allows it to operate even when the system is non-invertible. Using a mean-square-error metric, we strike an optimal balance between Fourier-domain regularization that is matched to the system and wavelet-domain regularization that is matched to the input signal. The resultant algorithm is fast, $O(N\log\sbsp{2}{2}N)$ where N denotes the number of samples, and is well-suited to signals and images with spatially-localized phenomena such as edges. In addition to enjoying asymptotically optimal rates of error decay for some systems, the algorithm also achieves excellent performance at fixed data lengths. In simulations with real data, the algorithm outperforms the conventional LTI Wiener filter and other wavelet-based deconvolution algorithms in terms of both visual quality and MSE performance.
dc.format.extent 59 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectStatistics
Biomedical engineering
Electronics
Electrical engineering
dc.title Wavelet-based deconvolution for ill-conditioned systems
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science
dc.identifier.citation Neelamani, Ramesh. "Wavelet-based deconvolution for ill-conditioned systems." (1999) Master’s Thesis, Rice University. https://hdl.handle.net/1911/17290.


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