deposit_your_work

Wavelet-Domain Regularized Deconvolution for Ill-Conditioned Systems

Files in this item

Files Size Format View
Nee1999Oct5Wavelet-do.PDF 750.7Kb application/pdf Thumbnail
Nee1999Oct5Wavelet-do.PS 842.9Kb application/postscript View/Open

Show full item record

Item Metadata

Title: Wavelet-Domain Regularized Deconvolution for Ill-Conditioned Systems
Author: Neelamani, Ramesh; Choi, Hyeokho; Baraniuk, Richard G.
Type: Conference Paper
Keywords: Fourier-domain; wavelet-based image deconvolution
Citation: R. Neelamani, H. Choi and R. G. Baraniuk,"Wavelet-Domain Regularized Deconvolution for Ill-Conditioned Systems," in IEEE International Conference on Image Processing,, pp. 204-208.
Abstract: We propose a hybrid approach to wavelet-based image 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 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. Theoretical analysis reveals that the optimal balance is determined by economics of the input signal wavelet representation and the operator structure. The resultant algorithm is fast, O(Nlog2 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.
Date Published: 1999-10-01

This item appears in the following Collection(s)

  • ECE Publications [1047 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.