Wavelet-Based Deconvolution for Ill-Conditioned Systems
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1995
- Accession Number
- ADA495095
Entities
People
- Hyeokho Choi
- Ramesh Neelamani
- Richard G. Baraniuk
Organizations
- Rice University