Filtered Variation method for denoising and sparse signal processing
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
3329 - 3332
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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. © 2012 IEEE.
projection onto convex sets
Projection onto convex sets
Projections onto convex sets
Permalink (Please cite this version)http://hdl.handle.net/11693/28155
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