Title :
Generalized Total Variation: Tying the Knots
Author :
Selesnick, Ivan W.
Author_Institution :
Dept. of Electr. & Comput. Eng., New York Univ., New York, NY, USA
Abstract :
This letter formulates a convex generalized total variation functional for the estimation of discontinuous piecewise linear signals from corrupted data. The method is based on (1) promoting pairwise group sparsity of the second derivative signal and (2) decoupling the principle knot parameters so they can be separately weighted. The proposed method refines the recent approach by Ongie and Jacob.
Keywords :
convex programming; piecewise linear techniques; signal processing; variational techniques; convex generalized total variation functional; discontinuous piecewise linear signal estimation; principle knot parameter decoupling; second derivative signal pairwise group sparsity; Estimation; Jacobian matrices; Noise measurement; Noise reduction; Polynomials; Signal processing algorithms; TV; Denoising; sparse optimization; total variation;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2015.2449297
