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Proceedings Paper

Which wavelet bases are the best for image denoising?
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Paper Abstract

We use a comprehensive set of non-redundant orthogonal wavelet transforms and apply a denoising method called SUREshrink in each individual wavelet subband to denoise images corrupted by additive Gaussian white noise. We show that, for various images and a wide range of input noise levels, the orthogonal fractional (α, τ)-B-splines give the best peak signal-to-noise ratio (PSNR), as compared to standard wavelet bases (Daubechies wavelets, symlets and coiflets). Moreover, the selection of the best set (α, τ) can be performed on the MSE estimate (SURE) itself, not on the actual MSE (Oracle). Finally, the use of complex-valued fractional B-splines leads to even more significant improvements; they also outperform the complex Daubechies wavelets.

Paper Details

Date Published: 17 September 2005
PDF: 12 pages
Proc. SPIE 5914, Wavelets XI, 59140E (17 September 2005); doi: 10.1117/12.614999
Show Author Affiliations
Florian Luisier, Ecole Polytechnique Federale de Lausanne (Switzerland)
Thierry Blu, Ecole Polytechnique Federale de Lausanne (Switzerland)
Brigitte Forster, Munich Univ. of Technology (Germany)
Michael Unser, Ecole Polytechnique Federale de Lausanne (Switzerland)


Published in SPIE Proceedings Vol. 5914:
Wavelets XI
Manos Papadakis; Andrew F. Laine; Michael A. Unser, Editor(s)

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