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TV_OnTheFly ***************************************************************************************************************** * author: Jordan Frecon * * institution: Univ Lyon, Ens de Lyon, Univ Claude Bernard, CNRS, Laboratoire de Physique, F-69342 Lyon, France * * date: April 19 2016 * * License CeCILL-B * ***************************************************************************************************************** ********************************************************* * RECOMMENDATIONS: * * This toolbox is designed to work with Matlab 2015.a * ********************************************************* ------------------------------------------------------------------------------------------------------------------------ DESCRIPTION: This toolbox provides an efficient on-the-fly (yet approximate) solution of the $\ell_{1,2}$-TV minimization problem. The corresponding method is based on the local validation of the Karush-Kuhn-Tucker conditions on the dual problem. This toolbox consists of 1 subfolder containing MATLAB functions designed for the proposed algorithm. ------------------------------------------------------------------------------------------------------------------------ SPECIFICATIONS for using TV_OnTheFly: One demo file 'demo_TV_OnTheFly.m' is proposed. It provides one denoising example of a multivariate signal (with M components) Two display setting are proposed depending on the variable ‘param.disp’, namely: 1) Offline setting, showing the solution for a given observation. 2) Online setting, showing the solution as the observation stream incomes. The main function is TV_OnTheFly.m. ------------------------------------------------------------------------------------------------------------------------ RELATED PUBLICATION: # J. Frecon, N. Pustelnik, P. Abry, L. Condat On-The-Fly Approximation of Multivariate Total Variation Minimization IEEE Transactions on Signal Processing, Vol. 64, Issue 9, pp. 2355-2364, May. 2016 ------------------------------------------------------------------------------------------------------------------------
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Efficient on-the-fly (yet approximate) solution of the l_12-TV minimization problem
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