Filtered Variation method for denoising and sparse signal processing

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.


Présenté à:
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, Kyoto, Japan, March 25-30, 2012
Année
2012
Laboratoires:




 Notice créée le 2015-02-02, modifiée le 2019-08-12

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