Generalized Total Variation Denoising Via Augmented Lagrangian Cycle Spinning With Haar Wavelets

We consider the denoising of signals and images using regularized least-squares method. In particular, we propose a simple minimization algorithm for regularizers that are functions of the discrete gradient. By exploiting the connection of the discrete gradient with the Haar-wavelet transform, the n-dimensional vector minimization can be decoupled into n scalar minimizations. The proposed method can efficiently solve total-variation (TV) denoising by iteratively shrinking shifted Haar-wavelet transforms. Furthermore, the decoupling naturally lends itself to extensions beyond l(1) regularizers.


Published in:
2012 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 909-912
Presented at:
IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, JAPAN, MAR 25-30, 2012
Year:
2012
Publisher:
New York, Ieee
ISBN:
978-1-4673-0046-9
Keywords:
Laboratories:




 Record created 2013-03-28, last modified 2018-03-17

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