高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于多尺度生成对抗网络的运动散焦红外图像复原

易诗 吴志娟 朱竞铭 李欣荣 袁学松

易诗, 吴志娟, 朱竞铭, 李欣荣, 袁学松. 基于多尺度生成对抗网络的运动散焦红外图像复原[J]. 电子与信息学报, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495
引用本文: 易诗, 吴志娟, 朱竞铭, 李欣荣, 袁学松. 基于多尺度生成对抗网络的运动散焦红外图像复原[J]. 电子与信息学报, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495
Shi YI, Zhijuan WU, Jingming ZHU, Xinrong LI, Xuesong YUAN. Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495
Citation: Shi YI, Zhijuan WU, Jingming ZHU, Xinrong LI, Xuesong YUAN. Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495

基于多尺度生成对抗网络的运动散焦红外图像复原

doi: 10.11999/JEIT190495
基金项目: 国家自然科学基金(61771096)
详细信息
    作者简介:

    易诗:男,1983年生,高级实验师,研究方向为深度学习、红外图像处理

    袁学松:男,1980年生,教授,研究方向为太赫兹成像技术、信号与信息处理

    通讯作者:

    易诗 549745481@qq.com

  • 中图分类号: TN911.73

Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network

Funds: The National Natural Science Foundation of China (61771096)
  • 摘要:

    红外热成像系统在夜间实施目标识别与检测优势明显,而移动平台上动态环境所导致的运动散焦模糊影响上述成像系统的应用。该文针对上述问题,基于生成对抗网络开展运动散焦后红外图像复原方法研究,采用生成对抗网络抑制红外图像的运动散焦模糊,提出一种针对红外图像的多尺度生成对抗网络(IMdeblurGAN)在高效抑制红外图像运动散焦模糊的同时保持红外图像细节对比度,提升移动平台上夜间目标的检测与识别能力。实验结果表明:该方法相对已有最优模糊图像复原方法,图像峰值信噪比(PSNR)提升5%,图像结构相似性(SSIMx)提升4%,目标识别YOLO置信度评分提升6%。

  • 图  1  散焦模糊轨迹

    图  2  生成对抗网络还原运动散焦红外图像

    图  3  生成网络结构

    图  4  判别网络结构

    图  5  复原效果对比

    图  6  复原细节对比测试

    图  7  YOLOV3置信度对比

    表  1  复原性能对比分析

    复原方法平均峰值信噪比 (dB)平均结构相似性
    Wiener21.30.62
    LR22.50.65
    DeblurGAN27.00.75
    SRN-DeblurNet30.50.88
    本文IMdeblurGAN32.00.92
    下载: 导出CSV

    表  2  YOLO V3置信度对比分析

    原始图像运动散焦图像Wiener逆滤波LR迭代去卷积DeblurGANSRN-DeblurNet本文IMdeblurGAN
    YOLOV3 评分0.97不能识别不能识别不能识别0.770.890.95
    下载: 导出CSV
  • 崔美玉. 论红外热像仪的应用领域及技术特点[J]. 中国安防, 2014(12): 90–93. doi: 10.3969/j.issn.1673-7873.2014.12.026

    CUI Meiyu. On the application field and technical characteristics of infrared thermal imager[J]. China Security &Protection, 2014(12): 90–93. doi: 10.3969/j.issn.1673-7873.2014.12.026
    KUPYN O, BUDZAN V, MYKHAILYCH M, et al. DeblurGAN: Blind motion deblurring using conditional adversarial networks[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 8183–8192.
    TAO Xin, GAO Hongyun, SHEN Xiaoyong, et al. Scale-recurrent network for deep image deblurring[C]. The 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 8174–8182.
    HE Zewei, CAO Yanpeng, DONG Yafei, et al. Single-image-based nonuniformity correction of uncooled long-wave infrared detectors: A deep-learning approach[J]. Applied Optics, 2018, 57(18): D155–D164. doi: 10.1364/AO.57.00D155
    邵保泰, 汤心溢, 金璐, 等. 基于生成对抗网络的单帧红外图像超分辨算法[J]. 红外与毫米波学报, 2018, 37(4): 427–432. doi: 10.11972/j.issn.1001-9014.2018.04.009

    SHAO Baotai, TANG Xinyi, JIN Lu, et al. Single frame infrared image super-resolution algorithm based on generative adversarial nets[J]. Journal of Infrared and Millimeter Wave, 2018, 37(4): 427–432. doi: 10.11972/j.issn.1001-9014.2018.04.009
    刘鹏飞, 赵怀慈, 曹飞道. 多尺度卷积神经网络的噪声模糊图像盲复原[J]. 红外与激光工程, 2019, 48(4): 0426001. doi: 10.3788/IRLA201948.0426001

    LIU Pengfei, ZHAO Huaici, and CAO Feidao. Blind deblurring of noisy and blurry images of multi-scale convolutional neural network[J]. Infrared and Laser Engineering, 2019, 48(4): 0426001. doi: 10.3788/IRLA201948.0426001
    BOUSMALIS K, SILBERMAN N, DOHAN D, et al. Unsupervised pixel-level domain adaptation with generative adversarial networks[C]. The 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 95–104.
    李凌霄, 冯华君, 赵巨峰, 等. 红外焦平面阵列的盲元自适应快速校正[J]. 光学精密工程, 2017, 25(4): 1009–1018. doi: 10.3788/OPE.20172504.1009

    LI Lingxiao, FENG Huajun, ZHAO Jufeng, et al. Adaptive and fast blind pixel correction of IRFPA[J]. Optics and Precision Engineering, 2017, 25(4): 1009–1018. doi: 10.3788/OPE.20172504.1009
    DONG Chao, LOY C, HE Kaiming, et al. Learning a deep convolutional network for image super-resolution[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 184–199.
    EFRAT N, GLASNER D, APARTSIN A, et al. Accurate blur models vs. image priors in single image super-resolution[C]. The 2013 IEEE International Conference on Computer Vision, Sydney, Australia, 2013: 2832–2839.
    HE Anfeng, LUO Chong, TIAN Xinmei, et al. A twofold Siamese network for real-time object tracking[C]. The 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 4834–4843.
    LIN Zhouchen and SHUM H Y. Fundamental limits of reconstruction-based superresolution algorithms under local translation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 83–97. doi: 10.1109/TPAMI.2004.1261081
    杨阳, 杨静宇. 基于显著性分割的红外行人检测[J]. 南京理工大学学报, 2013, 37(2): 251–256.

    YANG Yang and YANG Jingyu. Infrared pedestrian detection based on saliency segmentation[J]. Journal of Nanjing University of Science and Technology, 2013, 37(2): 251–256.
    PINNEGAR C R and MANSINHA L. Time-local spectral analysis for non-stationary time series: The S-transform for noisy signals[J]. Fluctuation and Noise Letters, 2003, 3(3): L357–L364. doi: 10.1142/S0219477503001439
    CAO Yanpeng and TISSE C L. Single-image-based solution for optics temperature-dependent nonuniformity correction in an uncooled long-wave infrared camera[J]. Optics Letters, 2014, 39(3): 646–648. doi: 10.1364/OL.39.000646
    REAL E, SHLENS J, MAZZOCCHI S, et al. YouTube-boundingboxes: A large high-precision human-annotated data set for object detection in video[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 7464–7473.
    WU Yi, LIM J, and YANG M H. Online object tracking: A benchmark[C]. The 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2411–2418. doi: 10.1109/CVPR.2013.312.
    WANG Zhou, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600–612. doi: 10.1109/TIP.2003.819861
    KIM J, LEE J K, and LEE K M. Accurate image super-resolution using very deep convolutional networks[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1646–1654. doi: 10.1109/CVPR.2016.182.
    KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84–90. doi: 10.1145/3065386
  • 加载中
图(7) / 表(2)
计量
  • 文章访问数:  2991
  • HTML全文浏览量:  1352
  • PDF下载量:  119
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-07-03
  • 修回日期:  2020-01-22
  • 网络出版日期:  2020-03-25
  • 刊出日期:  2020-07-23

目录

    /

    返回文章
    返回