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Multichannel SURE-LET Wavelet Denoising

Florian Luisier and Thierry Blu


We extend the denoising approach initially developed for grayscale images, to multichannel (e.g., color) images. A non-redundant---orthonormal---wavelet transform is first applied to the noisy data, followed by the (subband-dependent) thresholding of individual wavelet coefficients which are finally brought back to the image domain by inverse wavelet transform. This wavelet thresholding function is pointwise and depends on the coefficients of same location in the other channels, as well as on their parents in the coarser wavelet subband. Two key ingredients make this approach novel, feasible, and even computationally efficient:

Thanks to the SURE, no a priori image model is needed to optimize the denoising process, which then merely amounts to solving a linear system of equations in each wavelet subband.

Extensive comparisons with the state-of-the-art multiresolution image denoising algorithms indicate that despite being non-redundant, our algorithm matches the quality of the best redundant approaches, and even outperforms them when the number of channels increases.


[1] F. Luisier, T. Blu, "SURE-LET Multichannel Image Denoising: Interscale Orthonormal Wavelet Thresholding," IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 482-492, April 2008.

Matlab Software

The Matlab code available here is the algorithm described in [1]. This package implements the multichannel SURE-LET principle applied to interscale orthonormal wavelet thresholding. Download the zip archive. To understand how to use these files, please read the file README.txt or the online help in the routines.

If you have any comments, please feel free to contact: Florian Luisier

Java Applet Demonstration


Conditions of use

You are free to use this software for research purposes, but you should not redistribute it without our consent. In addition, we expect you to include an adequate citation and acknowledgment whenever you present or publish results that are based on it.

© 2022 EPFL • • 11.08.2022