The Study on Multi-exposure Image Fusion of Finger Vein

被引:0
|
作者
Wang, Chen [1 ]
Fang, Peiyu [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China
关键词
Finger vein; multi-exposure image fusion; camera response curve; Laplacian pyramid;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
For the finger vein recognition technology, the image of the finger vein is seriously missing, and the brightness contrast is not very obvious. A multi-exposure image fusion algorithm based on the camera response curve is proposed. Gaussian pyramid decomposition is performed on multiple finger vein images of different exposures in the same scene, the amount of information of the pixels is measured according to the camera response curve, and the Gaussian equation constrains the brightness of the fused image. Then a weight correction function is proposed to avoid the loss of image detail in overexposed or underexposed regions, and finally multi-scale and multi-resolution fusion of the image through Laplacian pyramid. Finally, according to the algorithm proposed in this paper, several sets of actual finger vein images were collected and analyzed. The effects of the algorithm were analyzed and evaluated from subjective and objective aspects. Experiments show that the proposed algorithm is simple and feasible, which not only preserves the image details of overexposed and under-exposed areas in the source image set, but also minimizes distortion.
引用
收藏
页码:848 / 852
页数:5
相关论文
共 50 条
  • [1] MULTI-EXPOSURE IMAGE FUSION BASED ON EXPOSURE COMPENSATION
    Kinoshita, Yuma
    Yoshida, Taichi
    Shiota, Sayaka
    Kiya, Hitoshi
    2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, : 1388 - 1392
  • [2] AUTOMATIC EXPOSURE COMPENSATION FOR MULTI-EXPOSURE IMAGE FUSION
    Kinoshita, Yuma
    Shiota, Sayaka
    Kiya, Hitoshi
    2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2018, : 883 - 887
  • [3] Enhancing image visuality by multi-exposure fusion
    Yan, Qingsen
    Zhu, Yu
    Zhou, Yulin
    Sun, Jinqiu
    Zhang, Lei
    Zhang, Yanning
    PATTERN RECOGNITION LETTERS, 2019, 127 : 66 - 75
  • [4] A novel fusion approach of multi-exposure image
    Kong, Jun
    Wang, Rujuan
    Lu, Yingha
    Feng, Xue
    Zhang, Jingbuo
    EUROCON 2007: THE INTERNATIONAL CONFERENCE ON COMPUTER AS A TOOL, VOLS 1-6, 2007, : 1458 - 1464
  • [5] An Improved Multi-Exposure Image Fusion Algorithm
    Xiang, Huyan
    Ma Xi-rong
    MEMS, NANO AND SMART SYSTEMS, PTS 1-6, 2012, 403-408 : 2200 - 2205
  • [6] Review of Multi-Exposure Image Fusion Methods
    Zhu Xinli
    Zhang Yasheng
    Fang Yuqiang
    Zhang Xitao
    Xu Jieping
    Luo Di
    LASER & OPTOELECTRONICS PROGRESS, 2023, 60 (22)
  • [7] A Method for Fast Multi-Exposure Image Fusion
    Choi, Seungcheol
    Kwon, Oh-Jin
    Lee, Jinhee
    IEEE ACCESS, 2017, 5 : 7371 - 7380
  • [8] EMEF: Ensemble Multi-Exposure Image Fusion
    Liu, Renshuai
    Li, Chengyang
    Cao, Haitao
    Zheng, Yinglin
    Zeng, Ming
    Cheng, Xuan
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 2, 2023, : 1710 - 1718
  • [9] Detail preserving multi-exposure image fusion
    Li W.-Z.
    Yi B.-S.
    Qiu K.
    Peng H.
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2016, 24 (09): : 2283 - 2292
  • [10] A new multi-exposure image fusion method
    Yang, Longpei
    Jiang, Chunhua
    Rao, Yunbo
    Lu, Linlin
    Chen, Ping
    Shao, Jun
    Journal of Computational Information Systems, 2015, 11 (09): : 3245 - 3256