A fixed-point algorithm for second-order total variation models in image denoising
In this paper, we construct fixed-point algorithms for the second-order total variation models through discretization models and the subdifferential and proximity operators. Particularly, we focus on the convergence conditions of our algorithms by analyzing the eigenvalues of the difference matrix....
Saved in:
Published in | Computational & applied mathematics Vol. 38; no. 1; pp. 1 - 14 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.03.2019
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | In this paper, we construct fixed-point algorithms for the second-order total variation models through discretization models and the subdifferential and proximity operators. Particularly, we focus on the convergence conditions of our algorithms by analyzing the eigenvalues of the difference matrix. The algorithms are tested on various images to verify our proposed convergence conditions. The experiments compared with the split Bregman algorithms demonstrate that fixed-point algorithms could solve the second-order functional minimization problem stably and effectively. |
---|---|
ISSN: | 2238-3603 1807-0302 |
DOI: | 10.1007/s40314-019-0763-2 |