Non-convex Total Variation Regularization for Convex Denoising of Signals

• Published in: Journal of mathematical imaging and vision Vol. 62; no. 6-7; pp. 825 - 841
• Main Authors: Selesnick, Ivan, Lanza, Alessandro, Morigi, Serena, Sgallari, Fiorella
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2020; Springer Nature B.V
• Subjects: Algorithms; Applications of Mathematics; Computer Science; Convexity; Cost function; Image Processing and Computer Vision; Mathematical Methods in Physics; Noise reduction; Parameters; Random noise; Regularization; Signal,Image and Speech Processing; Forward-backward splitting algorithm; Signal denoising; Total variation regularization; Convex non-convex regularization

Total variation (TV) signal denoising is a popular nonlinear filtering method to estimate piecewise constant signals corrupted by additive white Gaussian noise. Following a ‘convex non-convex’ strategy, recent papers have introduced non-convex regularizers for signal denoising that preserve the conv...