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

Authors Organisations
  • Jing He(Author)
    Hangzhou Dianzi University
  • Lei Shen(Author)
    Hangzhou Dianzi University
  • Yudong Yao(Author)
    Stevens Institute of Technology
  • Huaxia Wang(Author)
    Oklahoma State University - Stillwater
  • Guodong Zhao(Author)
    Top Glory Tech Limited Company
  • Xiaowei Gu(Author)
  • Weiping Ding(Author)
    Nantong University
Type Article
Original languageEnglish
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Early online date23 Sep 2021
Publication statusE-pub ahead of print - 23 Sep 2021
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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.


  • Convolution, Finger vein, GAN, Generators, image deblurring, Image restoration, Kernel, Mathematical models, texture loss, Training, Veins