Convexity-edge-preserving Signal Recovery with Linearly Involved Generalized Minimax Concave Penalty Function

Jiro Abe; Masao Yamagishi; Isao Yamada

ICASSP 2019accepted0 citations

Convexity-edge-preserving Signal Recovery with Linearly Involved Generalized Minimax Concave Penalty Function

Abstract

In this paper, we propose a new linearly involved convexity-preserving model for signal recovery by extending the idea in the generalized minimax concave (GMC) penalty [Se-lesnick' 17]. The proposed model can use nonconvex penalties but maintain the overall convexity and is applicable to much more general scenarios of signal recovery than the original GMC model. We also propose a new iterative algorithm which has theoretical guarantee of convergence to a global minimizer of the proposed model. A numerical experiment for noise suppression shows excellent edge-preserving performance of the proposed smoother in comparison with the standard convex TV smoother.

BibTeX

@inproceedings{icassp2019_convexityedgepre,
  title = {Convexity-edge-preserving Signal Recovery with Linearly Involved Generalized Minimax Concave Penalty Function},
  author = {Jiro Abe and Masao Yamagishi and Isao Yamada},
  booktitle = {ICASSP 2019},
  year = {2019}
}