Kamilov, UlugbekBostan, EmrahUnser, Michael2013-03-282013-03-282013-03-28201210.1109/ICASSP.2012.6288032https://infoscience.epfl.ch/handle/20.500.14299/90623WOS:000312381401008We 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.signal denoisingsoft-thresholdingcycle spinningTV denoisingtext::conference output::conference proceedings::conference paper