文件列表:

tgv\common\sfigure.m (290, 2013-07-10)
tgv\common\dxm.m (100, 2013-07-10)
tgv\common\dym.m (99, 2013-07-10)
tgv\common\dxp.m (72, 2013-07-10)
tgv\common\dyp.m (73, 2013-07-10)
tgv\common\compute_psnr.m (123, 2013-07-10)
tgv\data\watercastle.png (86227, 2013-07-10)
tgv\ncLogTGVL2_IRL1.m (3166, 2013-07-10)
tgv\ncLogTVL2_IRL1.m (2010, 2013-07-10)
tgv\ncpnormTGVL2_IRL1.m (3257, 2013-07-10)
tgv\ncpnormTVL2_IRL1.m (2074, 2013-07-10)
tgv\primal_dual_TGVL2.m (1721, 2013-07-10)
tgv\primal_dual_TVL2.m (1060, 2013-07-10)
tgv\test_denoising.m (2589, 2013-07-10)

Matlab code for iteratively reweighted l1 optimization for non-smooth non-convex optimization Copyright (c) 2013 Peter Ochs ------------------------------ Terms of use ------------------------------ This program is provided for research purposes only. Any commercial use is prohibited. If you are interested in a commercial use, please contact the copyright holder. If you used this program or part of this program in your research work, you should cite the following publication: Peter Ochs, Alexey Dosovitskiy, Thomas Brox, Thomas Pock An iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2013. This program is distributed WITHOUT ANY WARRANTY. -------------------------------------------------------------- Example Code for denoising with non-convex regularization term -------------------------------------------------------------- test_denoising.m: This file executes the denoising models with quadratic penalty on the data term and different convex and non-convex regularization terms: - primal_dual_TVL2.m: total variation penalty - primal_dual_TGVL2.m: total generalized variation penalty (of order 2) - ncLogTVL2_IRL1.m: penalty prototype log( 1 + beta*|grad u| ) - ncLogTGVL2_IRL1.m: penalty prototype log( 1 + beta*|grad u - v| ) + log( 1 + beta*|grad v| ) - ncpnormTVL2_IRL1.m: penalty prototype ( |grad u|+eps )^p with 0

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