doi:10.1109/ICASSP.2012.6288628
Kose, Kivanc
Cevher, Volkan
Cetin, A. Enis
Filtered Variation method for denoising and sparse signal processing
http://infoscience.epfl.ch/record/204953/files/FILTERED%20VARIATION%20METHOD.pdf
We 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.
2015-02-02T13:30:18Z
http://infoscience.epfl.ch/record/204953
http://infoscience.epfl.ch/record/204953
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