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Finger Vein Image Deblurring Using Neighbors-based Binary-GAN (NB-GAN)

He, Jing, Shen, Lei, Yao, YuDong, Wang, HuaXia, Zhao, GuoDong, Gu, Xiaowei, Ding, Weiping (2021) Finger Vein Image Deblurring Using Neighbors-based Binary-GAN (NB-GAN). IEEE Transactions on Emerging Topics in Computational Intelligence, . ISSN 2471-285X. (In press) (KAR id:90399)

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Abstract

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 09:47 UTC
Last Modified: 29 Sep 2021 11:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90399 (The current URI for this page, for reference purposes)
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