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

基于深度引导与自学习的高动态成像算法

张俊超 杨飞帆 时伟 陈溅来 赵党军 杨德贵

张俊超, 杨飞帆, 时伟, 陈溅来, 赵党军, 杨德贵. 基于深度引导与自学习的高动态成像算法[J]. 电子与信息学报, 2023, 45(1): 291-299. doi: 10.11999/JEIT211188
引用本文: 张俊超, 杨飞帆, 时伟, 陈溅来, 赵党军, 杨德贵. 基于深度引导与自学习的高动态成像算法[J]. 电子与信息学报, 2023, 45(1): 291-299. doi: 10.11999/JEIT211188
ZHANG Junchao, YANG Feifan, SHI Wei, CHEN Jianlai, ZHAO Dangjun, YANG Degui. Method for High Dynamic Range Imaging Based on Deep Guided and Self-learning[J]. Journal of Electronics & Information Technology, 2023, 45(1): 291-299. doi: 10.11999/JEIT211188
Citation: ZHANG Junchao, YANG Feifan, SHI Wei, CHEN Jianlai, ZHAO Dangjun, YANG Degui. Method for High Dynamic Range Imaging Based on Deep Guided and Self-learning[J]. Journal of Electronics & Information Technology, 2023, 45(1): 291-299. doi: 10.11999/JEIT211188

基于深度引导与自学习的高动态成像算法

doi: 10.11999/JEIT211188
基金项目: 国家自然科学基金(62105372, 61901531),国防科技重点实验室基金(6142401200301),湖南省自然科学基金(2021JJ40794, 2021JJ40781)
详细信息
    作者简介:

    张俊超:男,博士,讲师,研究方向为图像处理与机器学习

    杨飞帆:男,博士生,研究方向为图像处理与模式识别

    时伟:男,博士,副教授,研究方向为图像处理与智能控制

    陈溅来:男,博士,副教授,研究方向为雷达成像与信号处理

    赵党军:男,博士,副教授,研究方向为飞行器控制与信号处理

    杨德贵:男,博士,教授,研究方向为雷达信号处理与图像处理

    通讯作者:

    时伟 ahshw@csu.edu.cn

  • 1)https://github.com/hangxiaotian/Perceptual-Multi-exposure-Image-Fusion
  • 中图分类号: TN911.73; TP391.4

Method for High Dynamic Range Imaging Based on Deep Guided and Self-learning

Funds: The National Natural Science Foundation of China (62105372, 61901531), The Foundation of Key Laboratory of National Defense Science and Technology (6142401200301), The Natural Science Foundation of Hunan Province (2021JJ40794, 2021JJ40781)
  • 摘要: 多曝光图像融合是将同一场景不同曝光度的图像进行融合,是当前高动态场景成像的主流方法。为了获得更自然的融合效果,该文提出基于深度引导与自学习的多曝光图像融合网络(MEF-Net)。该网络是以端到端的方式融合任意数量的不同曝光度图像,无监督地输出最优的融合结果。在损失函数方面,通过引入强度保真约束项和加权的多曝光图像融合结构相似度(MEF-SSIM),提升融合效果。此外,针对两幅极度曝光情况下的图像融合,该文采用自学习的方式,基于预训练的模型进行参数微调与优化,减弱光晕现象。基于大量测试数据,实验结果表明,该文所提算法在定量指标和视觉融合效果方面均优于现有主流算法。
    1)  1)https://github.com/hangxiaotian/Perceptual-Multi-exposure-Image-Fusion
  • 图  1  本文网络结构

    图  2  CAN网络结构

    图  3  自学习过程

    图  4  多曝光图像融合实验结果

    图  5  不同算法多曝光图像融合的MEF-SSIM结果

    图  6  两曝光图像(Chapel)融合实验结果

    图  7  两曝光图像(Desk)融合实验结果

    图  8  不同算法两曝光图像融合的MEF-SSIM结果

    图  9  不同参数下的融合结果(Desk)

    图  10  不同参数下的融合结果(Room)

  • [1] 李卫中, 易本顺, 邱康, 等. 细节保留的多曝光图像融合[J]. 光学 精密工程, 2016, 24(9): 2283–2292. doi: 10.3788/OPE.20162409.2283

    LI Weizhong, YI Benshun, QIU Kang, et al. Detail preserving multi-exposure image fusion[J]. Optics and Precision Engineering, 2016, 24(9): 2283–2292. doi: 10.3788/OPE.20162409.2283
    [2] 孙婧, 徐岩, 段绿茵, 等. 高动态范围(HDR)技术综述[J]. 信息技术, 2016(5): 41–45,49. doi: 10.13274/j.cnki.hdzj.2016.05.012

    SUN Jing, XU Yan, DUAN Lvyin, et al. A survey on high dynamic range display technology[J]. Information Technology, 2016(5): 41–45,49. doi: 10.13274/j.cnki.hdzj.2016.05.012
    [3] MARCHESSOUX C, DE PAEPE L, VANOVERMEIRE O, et al. Clinical evaluation of a medical high dynamic range display[J]. Medical Physics, 2016, 43(7): 4023–4031. doi: 10.1118/1.4953187
    [4] 王东. 基于卷积神经网络的高动态成像技术研究[D]. [硕士论文], 西安电子科技大学, 2020.

    WANG Dong. Investigation of high dynamic imaging technique based on convolutional neural network[D]. [Master dissertation], Xidian University, 2020.
    [5] 马夏一, 范方晴, 卢陶然, 等. 基于图像块分解的多曝光图像融合去鬼影算法[J]. 光学学报, 2019, 39(9): 0910001. doi: 10.3788/AOS201939.0910001

    MA Xiayi, FAN Fangqing, LU Taoran, et al. Multi-exposure image fusion de-ghosting algorithm based on image block decomposition[J]. Acta Optica Sinica, 2019, 39(9): 0910001. doi: 10.3788/AOS201939.0910001
    [6] SHEN Rui, CHENG I, SHI Jianbo, et al. Generalized random walks for fusion of multi-exposure images[J]. IEEE Transactions on Image Processing, 2011, 20(12): 3634–3646. doi: 10.1109/TIP.2011.2150235
    [7] HOU Xinglin, LUO Haibo, QI Feng, et al. Guided filter-based fusion method for multiexposure images[J]. Optical Engineering, 2016, 55(11): 113101. doi: 10.1117/1.OE.55.11.113101
    [8] LEE S H, PARK J S, and CHO N I. A multi-exposure image fusion based on the adaptive weights reflecting the relative pixel intensity and global gradient[C]. 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018: 1737–1741.
    [9] LI Hui, MA Kede, YONG Hongwei, et al. Fast multi-scale structural patch decomposition for multi-exposure image fusion[J]. IEEE Transactions on Image Processing, 2020, 29: 5805–5816. doi: 10.1109/TIP.2020.2987133
    [10] HOU Xinglin, ZHANG Junchao, and ZHOU Peipei. Reconstructing a high dynamic range image with a deeply unsupervised fusion model[J]. IEEE Photonics Journal, 2021, 13(2): 3900210. doi: 10.1109/JPHOT.2021.3058740
    [11] 江泽涛, 何玉婷. 基于卷积自编码器和残差块的红外与可见光图像融合方法[J]. 光学学报, 2019, 39(10): 1015001. doi: 10.3788/AOS201939.1015001

    JIANG Zetao and HE Yuting. Infrared and visible image fusion method based on convolutional auto-encoder and residual block[J]. Acta Optica Sinica, 2019, 39(10): 1015001. doi: 10.3788/AOS201939.1015001
    [12] 唐超影, 浦世亮, 叶鹏钊, 等. 基于卷积神经网络的低照度可见光与近红外图像融合[J]. 光学学报, 2020, 40(16): 1610001. doi: 10.3788/AOS202040.1610001

    TANG Chaoying, PU Shiliang, YE Pengzhao, et al. Fusion of low-illuminance visible and near-infrared images based on convolutional neural networks[J]. Acta Optica Sinica, 2020, 40(16): 1610001. doi: 10.3788/AOS202040.1610001
    [13] LI Hui and WU Xiaojun. DenseFuse: A fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 2019, 28(5): 2614–2623. doi: 10.1109/TIP.2018.2887342
    [14] 聂茜茜, 肖斌, 毕秀丽, 等. 基于超像素级卷积神经网络的多聚焦图像融合算法[J]. 电子与信息学报, 2021, 43(4): 965–973. doi: 10.11999/JEIT191053

    NIE Xixi, XIAO Bin, BI Xiuli, et al. Multi-focus image fusion algorithm based on super pixel level convolutional neural network[J]. Journal of Electronics &Information Technology, 2021, 43(4): 965–973. doi: 10.11999/JEIT191053
    [15] MA Boyuan, ZHU Yu, YIN Xiang, et al. SESF-Fuse: An unsupervised deep model for multi-focus image fusion[J]. Neural Computing and Applications, 2021, 33(11): 5793–5804. doi: 10.1007/s00521-020-05358-9
    [16] ZHANG Junchao, SHAO Jianbo, CHEN Jianlai, et al. PFNet: An unsupervised deep network for polarization image fusion[J]. Optics Letters, 2020, 45(6): 1507–1510. doi: 10.1364/OL.384189
    [17] ZHANG Junchao, SHAO Jianbo, CHEN Jianlai, et al. Polarization image fusion with self-learned fusion strategy[J]. Pattern Recognition, 2021, 118: 108045. doi: 10.1016/j.patcog.2021.108045
    [18] CAI Jianrui, GU Shuhang, and ZHANG Lei. Learning a deep single image contrast enhancer from multi-exposure images[J]. IEEE Transactions on Image Processing, 2018, 27(4): 2049–2062. doi: 10.1109/TIP.2018.2794218
    [19] PRABHAKAR K R, SRIKAR V S, and BABU R V. DeepFuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs[C]. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017: 4724–4732.
    [20] JUNG H, KIM Y, JANG H, et al. Unsupervised deep image fusion with structure tensor representations[J]. IEEE Transactions on Image Processing, 2020, 29: 3845–3858. doi: 10.1109/TIP.2020.2966075
    [21] XU Han, MA Jiayi, JIANG Junjun, et al. U2Fusion: A unified unsupervised image fusion network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(1): 502–518. doi: 10.1109/TPAMI.2020.3012548
    [22] XU Han, MA Jiayi, and ZHANG Xiaoping. MEF-GAN: Multi-exposure image fusion via generative adversarial networks[J]. IEEE Transactions on Image Processing, 2020, 29: 7203–7216. doi: 10.1109/TIP.2020.2999855
    [23] MA Kede, DUANMU Zhengfang, ZHU Hanwei, et al. Deep guided learning for fast multi-exposure image fusion[J]. IEEE Transactions on Image Processing, 2020, 29: 2808–2819. doi: 10.1109/TIP.2019.2952716
    [24] YAN Qingsen, GONG Dong, SHI Qinfeng, et al. Attention-guided network for ghost-free high dynamic range imaging[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019: 1751–1760.
    [25] LIU Zhen, LIN Wenjie, LI Xinpeng, et al. ADNet: Attention-guided deformable convolutional network for high dynamic range imaging[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, USA, 2021: 463–470.
    [26] SHARIF S M A, NAQVI R A, BISWAS M, et al. A two-stage deep network for high dynamic range image reconstruction[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, USA, 2021: 550–559.
    [27] HE Kaiming, SUN Jian, and TANG Xiaoou. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397–1409. doi: 10.1109/TPAMI.2012.213
    [28] MA Kede, ZENG Kai, and WANG Zhou. Perceptual quality assessment for multi-exposure image fusion[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3345–3356. doi: 10.1109/TIP.2015.2442920
  • 加载中
图(10)
计量
  • 文章访问数:  251
  • HTML全文浏览量:  133
  • PDF下载量:  65
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-10-28
  • 修回日期:  2022-03-24
  • 网络出版日期:  2022-03-30
  • 刊出日期:  2023-01-17

目录

    /

    返回文章
    返回