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17 September 2005 Which wavelet bases are the best for image denoising?
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Proceedings Volume 5914, Wavelets XI; 59140E (2005)
Event: Optics and Photonics 2005, 2005, San Diego, California, United States
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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Florian Luisier, Thierry Blu, Brigitte Forster, and Michael Unser "Which wavelet bases are the best for image denoising?", Proc. SPIE 5914, Wavelets XI, 59140E (17 September 2005);

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