Wavelet-based Deconvolution for Ill-conditioned Systems
| dc.citation.bibtexName | inproceedings | en_US |
| dc.citation.conferenceName | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) | en_US |
| dc.citation.firstpage | 3241 | |
| dc.citation.lastpage | 3244 | |
| dc.citation.location | Phoenix, AZ | en_US |
| dc.citation.volumeNumber | 6 | en_US |
| dc.contributor.author | Neelamani, Ramesh | en_US |
| dc.contributor.author | Choi, Hyeokho | en_US |
| dc.contributor.author | Baraniuk, Richard G. | en_US |
| dc.contributor.org | Center for Multimedia Communications (http://cmc.rice.edu/) | en_US |
| dc.contributor.org | Digital Signal Processing (http://dsp.rice.edu/) | en_US |
| dc.date.accessioned | 2007-10-31T00:55:12Z | |
| dc.date.available | 2007-10-31T00:55:12Z | |
| dc.date.issued | 1999-03-01 | en |
| dc.date.modified | 2006-06-21 | en_US |
| dc.date.note | 2001-08-27 | en_US |
| dc.date.submitted | 1999-03-01 | en_US |
| dc.description | Conference Paper | en_US |
| dc.description.abstract | In 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.sponsorship | Texas Instruments | en_US |
| dc.description.sponsorship | Defense Advanced Research Projects Agency | en_US |
| dc.description.sponsorship | National Science Foundation | en_US |
| dc.identifier.citation | R. Neelamani, H. Choi and R. G. Baraniuk, "Wavelet-based Deconvolution for Ill-conditioned Systems," vol. 6, 1999. | |
| dc.identifier.doi | http://dx.doi.org/10.1109/ICASSP.1999.757532 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/20135 | |
| dc.language.iso | eng | |
| dc.subject | wavelet-based deconvolution | * |
| dc.subject | Fourier-domain system | * |
| dc.subject | LTI Wiener filter | * |
| dc.subject | MSE performance | * |
| dc.subject | wavelet-domain regularization | * |
| dc.subject.keyword | wavelet-based deconvolution | en_US |
| dc.subject.keyword | Fourier-domain system | en_US |
| dc.subject.keyword | LTI Wiener filter | en_US |
| dc.subject.keyword | MSE performance | en_US |
| dc.subject.keyword | wavelet-domain regularization | en_US |
| dc.subject.other | Image Processing and Pattern analysis | en_US |
| dc.subject.other | Wavelet based Signal/Image Processing | en_US |
| dc.subject.other | Multiscale Methods | en_US |
| dc.title | Wavelet-based Deconvolution for Ill-conditioned Systems | en_US |
| dc.type | Conference paper | |
| dc.type.dcmi | Text |