Which wavelet bases are the best for image denoising? | Zendy

Florian Luisier | Zendy; Thierry Blu | Zendy; Brigitte Forster | Zendy; Michaël Unser | Zendy

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Which wavelet bases are the best for image denoising?

Author(s) -

Florian Luisier,

Thierry Blu,

Brigitte Forster,

Michaël Unser

Publication year - 2005

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.614999

Subject(s) - wavelet , daubechies wavelet , mathematics , artificial intelligence , additive white gaussian noise , noise reduction , pattern recognition (psychology) , wavelet transform , orthogonal wavelet , gaussian noise , multiresolution analysis , noise (video) , computer science , white noise , algorithm , wavelet packet decomposition , image (mathematics) , statistics

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 (, )-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 (, ) 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. Keywords: Wavelet transform, image denoising, wavelet thresholding, wavelets choice

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