Which wavelet bases are the best for image denoising?

Florian Luisier; Thierry Blu; Brigitte Forster; Michael Unser

doi:10.1117/12.614999

17 September 2005 Which wavelet bases are the best for image denoising?

Florian Luisier, Thierry Blu, Brigitte Forster, Michael Unser

Proceedings Volume 5914, Wavelets XI; 59140E (2005) https://doi.org/10.1117/12.614999
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

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.

Citation Download Citation

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); https://doi.org/10.1117/12.614999

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