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Volume 43 Issue 4
Apr.  2021
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ZHANG Pengcheng, HE Mingxia, CHEN Shuo, ZHANG Hongzhen, ZHANG Xinxin. Terahertz Image Enhancement Based on Generative Adversarial Network[J]. Infrared Technology , 2021, 43(4): 391-396.
Citation: ZHANG Pengcheng, HE Mingxia, CHEN Shuo, ZHANG Hongzhen, ZHANG Xinxin. Terahertz Image Enhancement Based on Generative Adversarial Network[J]. Infrared Technology , 2021, 43(4): 391-396.
shu

Terahertz Image Enhancement Based on Generative Adversarial Network

  • 1.

    State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China

  • 2.

    School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China

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  • Received Date: September 28, 2019
  • Revised Date: November 03, 2019
    Graphical Abstract
    • Abstract
  • In terahertz scanning imaging, the image contrast is low due to laser power fluctuation and instrument vibration, and the imaging quality needs to be improved. At present, the processing of terahertz image is still in the traditional algorithm stage. In this paper, an image enhancement method based on Generative Adversarial Network is proposed, which includes the idea of deep learning. By introducing blur and noise into the training set image, the mapping relationship between low-quality images and high-quality images is learned and applied to real terahertz images. The experimental results show that, compared with traditional algorithms such as bilateral filtering and non-local mean filtering, this method can significantly improve the image contrast on the basis of improving image details, and has a good visual sense, which provides a new idea for terahertz image enhancement.
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    • References (16)
  • [1]
    郑显华, 王新柯, 孙文峰, 等. 太赫兹数字全息术的研发与应用[J]. 中国激光, 2014, 41(2): 0209003. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201402004.htm

    ZHENG Xianhua, WANG Xinke, SUN Wenfeng, et al. Developments and applications of the terahertz digital holography[J]. Chinese J. Lasers, 2014, 41(2): 0209003. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201402004.htm
    [2]
    张馨, 赵源萌, 张存林. 被动式太赫兹图像分割算法[J]. 强激光与粒子束, 2013, 25(6): 1597-1600. https://www.cnki.com.cn/Article/CJFDTOTAL-QJGY201306056.htm

    ZHANG Xin, ZHAO Yuanmeng, ZHANG Cunlin. Passive terahertz image segmentation algorithm[J]. High Power Laser and Particle Beams, 2013, 25(6): 1597-1600. https://www.cnki.com.cn/Article/CJFDTOTAL-QJGY201306056.htm
    [3]
    徐利民. 高分辨太赫兹图像处理[D]. 西安: 中国科学院研究生院(西安光学精密机械研究所), 2013.

    XU Limin. High Resolution Terahertz Image Processing[D]. Xi an: University of Chinese Academy of Sciences (Xi'an Institute of Optics and Precision Mechanics), 2013.
    [4]
    李琦, 杨永发, 胡佳琦. 一种用于太赫兹共焦扫描图像复原的复合算法[J]. 红外与激光工程, 2015, 44(1): 321-326. DOI: 10.3969/j.issn.1007-2276.2015.01.055

    LI Qi, YANG Yongfa, HU Jiaqi. A composite algorithm used for terahertz confocal scanning image restoration[J]. Infrared and Laser Engineering, 2015, 44(1): 321-326. DOI: 10.3969/j.issn.1007-2276.2015.01.055
    [5]
    杨永发, 李琦. 双边滤波算法的太赫兹共焦扫描图像去噪应用[J]. 激光与光电子学进展, 2015, 52(12): 121101. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201512015.htm

    YANG Yongfa, LI Qi. Application of bilateral filtering algorithm on terahertz confocal scanning image denoising[J]. Laser & Optoelectronics Progress, 2015, 52(12): 121101. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201512015.htm
    [6]
    DONG C, CHEN C L, HE K, et al. Learning a Deep Convolutional Network for Image Super-Resolution[M]//Computer Vision – ECCV Springer International Publishing, 2014: 184-199.
    [7]
    Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017: 4681-4690.
    [8]
    卢贺洋, 苏胜君, 袁明辉, 等. 太赫兹图像的超分辨率重建[J]. 红外技术, 2019, 41(1): 59-63. http://hwjs.nvir.cn/article/id/hwjs201901009

    LU Heyang, SU Shengjun, YUN Minghui, et al. Super-resolution reconstruction of terahertz image[J]. Infrared Technology, 2019, 41(1): 59-63. http://hwjs.nvir.cn/article/id/hwjs201901009
    [9]
    Johnson J, Alahi A, LI F. Perceptual losses for real-time style transfer and super-resolution[C]//In European Conference on Computer Vision (ECCV), Springer, 2016: 694-711.
    [10]
    Gross S, Wilber M. Training and investigating residual nets, online[EB/OL]. [2016-02-04]. http://torch.ch/blog/.
    [11]
    Shi W, Caballero J, Huszar F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 1874-1883.
    [12]
    Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[C]// International Conference on Learning Representations (ICLR), 2016: 1-16.
    [13]
    Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations (ICLR), 2015: 268-282.
    [14]
    Bruna J, Sprechmann P, Lecun Y. Super-resolution with deep convolutional sufficient statistics[C]//International Conference on Learning Representations (ICLR), 2016: 352-369.
    [15]
    Gatys L A, Ecker A S, Bethge M. Texture synthesis using convolutional neural networks[C]//Advances in Neural Information Processing Systems (NIPS), 2015: 262-270.
    [16]
    Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems (NIPS), 2014: 2672-2680.
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