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Title:
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Wavelet-based Deconvolution for Ill-conditioned Systems |
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Author:
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Neelamani, Ramesh; Choi, Hyeokho; Baraniuk, Richard G.
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Type:
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Conference Paper |
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Keywords:
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wavelet-based deconvolution; Fourier-domain system; LTI Wiener filter; MSE performance; wavelet-domain regularization |
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Citation:
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R. Neelamani, H. Choi and R. G. Baraniuk,"Wavelet-based Deconvolution for Ill-conditioned Systems," in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),, pp. 3241-3244. |
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Abstract:
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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. |
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Date Published:
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1999-03-01 |
