Accession Number:

ADA495095

Title:

Wavelet-Based Deconvolution for Ill-Conditioned Systems

Descriptive Note:

Corporate Author:

RICE UNIV HOUSTON TX DEPT OF ELECTRICAL AND COMPUTER ENGINEERING

Report Date:

1995-01-01

Pagination or Media Count:

5.0

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.

Subject Categories:

  • Numerical Mathematics
  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE