Fingerprint Image Denoising Via the Improved Total Variation (TV) Algorithm

DOI

10.2991/icsmim-15.2016.79How to use a DOI?

Keywords

Fingerprint image denoising, total variation, split Bregman iteration, relaxation factors.

Abstract

This paper proposes several improvements for a fingerprint image denoising method that is based on nonlocal total variation (TV) models using split Bregman iteration. The main improvement involves the addition of relaxation factors to the two-step iterative process of split Bregman iterative algorithms to obtain a double relaxation split Bregman iterative algorithm. The improved method is tested using numerous fingerprint images from FVC2004 databases. The experimental results show that the improved double relaxation split Bregman iterative algorithm achieves signi cantly better performance in terms of the visual subjective evaluation and the quantitative objective evaluation. The method achieves better noise suppression and effectively retains image edge details.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Rong Zhu
AU  - Yong Wang
AU  - Jingxing Liu
PY  - 2016/01
DA  - 2016/01
TI  - Fingerprint Image Denoising Via the Improved Total Variation (TV) Algorithm
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 427
EP  - 431
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.79
DO  - 10.2991/icsmim-15.2016.79
ID  - Zhu2016/01
ER  -