000204953 001__ 204953
000204953 005__ 20180317093628.0
000204953 0247_ $$2doi$$a10.1109/ICASSP.2012.6288628
000204953 037__ $$aCONF
000204953 245__ $$aFiltered Variation method for denoising and sparse signal processing
000204953 269__ $$a2012
000204953 260__ $$c2012
000204953 336__ $$aConference Papers
000204953 520__ $$aWe propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by using discrete time filters that are widely used in signal processing. We mathematically define the FV problem, and solve it using alternating projections in space and transform domains. We provide a globally convergent algorithm based on the projections onto convex sets approach. We apply to our algorithm to real denoising problems and compare it with the total variation recovery.
000204953 700__ $$aKose, Kivanc
000204953 700__ $$0243957$$aCevher, Volkan$$g199128
000204953 700__ $$aCetin, A. Enis
000204953 7112_ $$aInternational Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012$$cKyoto, Japan$$dMarch 25-30, 2012
000204953 8564_ $$s384457$$uhttps://infoscience.epfl.ch/record/204953/files/FILTERED%20VARIATION%20METHOD.pdf$$yn/a$$zn/a
000204953 909CO $$ooai:infoscience.tind.io:204953$$pSTI$$pconf
000204953 909C0 $$0252306$$pLIONS$$xU12179
000204953 917Z8 $$x231598
000204953 937__ $$aEPFL-CONF-204953
000204953 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000204953 980__ $$aCONF