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.
WOS:000312381401008
2012
978-1-4673-0046-9
New York
4
909
912
REVIEWED
Event name | Event place | Event date |
京都市 (Kyoto), Japan | MAR 25-30, 2012 | |