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基于轻量化渐进式残差网络的图像快速去模糊

杨爱萍 李磊磊 张兵 何宇清

杨爱萍, 李磊磊, 张兵, 何宇清. 基于轻量化渐进式残差网络的图像快速去模糊[J]. 电子与信息学报, 2022, 44(5): 1674-1682. doi: 10.11999/JEIT210298
引用本文: 杨爱萍, 李磊磊, 张兵, 何宇清. 基于轻量化渐进式残差网络的图像快速去模糊[J]. 电子与信息学报, 2022, 44(5): 1674-1682. doi: 10.11999/JEIT210298
YANG Aiping, LI Leilei, ZHANG Bing, HE Yuqing. Fast Image Deblurring Based On the Lightweight Progressive Residual Network[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1674-1682. doi: 10.11999/JEIT210298
Citation: YANG Aiping, LI Leilei, ZHANG Bing, HE Yuqing. Fast Image Deblurring Based On the Lightweight Progressive Residual Network[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1674-1682. doi: 10.11999/JEIT210298

基于轻量化渐进式残差网络的图像快速去模糊

doi: 10.11999/JEIT210298
基金项目: 国家自然科学基金(62071323, 61632018, 61771329)
详细信息
    作者简介:

    杨爱萍:女,1977年生,副教授,研究方向为深度学习、图像处理以及计算机视觉等

    李磊磊:男,1997年生,硕士生,研究方向为图像去模糊、图像超分辨率重建以及深度学习

    张兵:男,1994年生,硕士,研究方向为图像去模糊和深度学习

    何宇清:男,1973年生,讲师,研究方向为图像处理、模式识别等

    通讯作者:

    杨爱萍 yangaiping@tju.edu.cn

  • 中图分类号: TN911.73; TP391

Fast Image Deblurring Based On the Lightweight Progressive Residual Network

Funds: The National Natural Science Foundation of China (62071323, 61632018, 61771329)
  • 摘要: 基于深度学习的去模糊方法已经取得了较大进展,但是随着网络层数加深,去模糊网络需要更多的计算资源和内存消耗,难以用于实际场景。针对目前的去模糊网络参数量大、运算时间长等问题,该文设计了一种轻量快速的渐进式残差去模糊网络。该网络使用浅层残差网络作为基准模型,可充分利用图像的局部特征信息,加强反向传播时的信息流通。同时,通过多阶段递归调用残差网络并进行参数共享,可大大简化网络模型,减少网络参数。为了进一步提高去模糊网络的特征重建能力,该文引入特征重标定模块进行特征融合,对输入图像与各个残差网络的输出特征图进行通道加权,并对特征图的空间信息进行自适应选择,实现更好的特征重建。实验结果表明,所提算法网络模型参数量小、运行速度快,大幅度领先于现有算法,且对各种空域可变模糊去除均可实现理想复原效果。
  • 图  1  残差网络结构

    图  2  整体网络结构

    图  3  残差网络模块结构

    图  4  特征重标定模块结构

    图  5  本文去模糊算法流程图

    图  6  GOPRO数据集上实验结果

    图  7  本文算法去模糊实例

    图  8  特征重标定模块消融实验主观恢复结果对比

    图  9  残差模块递归次数消融实验主观恢复结果对比

    表  1  在合成数据集上PSNR和SSIM结果

    算法PSNR(dB)SSIM参数量(Mb)运行时间(s)
    Li等人[6]25.130.890012.17
    Nah等人[12]29.100.9130303.63.01
    Tao等人[14]30.100.932082.61.62
    Zhang等人[9]24.520.7926258.30
    Kupyn等人[4]28.700.927043.73.01
    Kupyn等人[17]29.550.9340244.50.35
    Gao等人 [16]30.920.942144.31.66
    本文算法30.210.933126.30.03
    下载: 导出CSV

    表  2  特征重标定模块消融实验

    PSNR(dB)参数量(Mb)
    无注意力30.0826.316
    通道注意力30.1626.319
    空间注意力30.1426.317
    通道+空间注意力30.2126.321
    下载: 导出CSV

    表  3  残差块递归次数消融实验

    递归次数
    567
    PSNR(dB)30.1730.2130.21
    参数量(Mb)20.06426.32130.712
    运行时间(s)0.020.030.07
    下载: 导出CSV
  • [1] LIANG Wang, HANG Yaping, LUO Siwei, et al. Deblurring Gaussian-blur images: A preprocessing for rail head surface defect detection[C]. 2011 IEEE International Conference on Service Operations, Logistics and Informatics, Beijing, China, 2011: 451–456.
    [2] MCCARTHY D M J, CHANDLER J H, and PALMERI A. Monitoring dynamic structural tests using image deblurring techniques[J]. Key Engineering Materials, 2013, 569/570: 932–939. doi: 10.4028/www.scientific.net/KEM.569-570.932
    [3] WANG Ge, SNYDER D L, O’SULLIVAN J A, et al. Iterative deblurring for CT metal artifact reduction[J]. IEEE Transactions on Medical Imaging, 1996, 15(5): 657–664. doi: 10.1109/42.538943
    [4] 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.
    [5] PAN Jinshan, SUN Deqing, PFISTER H, et al. Blind image deblurring using dark channel prior[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1628–1636.
    [6] LI Xu, ZHENG Shicheng, and JIA Jiaya. Unnatural L0 sparse representation for natural image deblurring[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 1107–1114.
    [7] PAN Jinshan, HU Zhe, SU Zhixun, et al. Deblurring text images via L0-regularized intensity and gradient prior[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 2901–2908.
    [8] ZHANG Hong, WU Yujie, ZHANG Lei, et al. Image deblurring using tri-segment intensity prior[J]. Neurocomputing, 2020, 398: 265–279. doi: 10.1016/j.neucom.2020.02.082
    [9] ZHANG Kai, ZUO Wangmeng, and ZHANG Lei. Learning a single convolutional super-resolution network for multiple degradations[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3262–3271.
    [10] VASU S, MALIGIREDDY V R, and RAJAGOPALAN A N. Non-blind deblurring: Handling kernel uncertainty with CNNs[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3272–3281.
    [11] SUN Jian, CAO Wenfei, XU Zongben, et al. Learning a convolutional neural network for non-uniform motion blur removal[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 769–777.
    [12] NAH S, KIM T H, and LEE K M. Deep multi-scale convolutional neural network for dynamic scene deblurring[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 257–265.
    [13] JAIN V and SEUNG H S. Natural image denoising with convolutional networks[C]. The 21st International Conference on Neural Information Processing Systems, Vancouver, Canada, 2008: 769–776.
    [14] TAO Xin, GAO Hongyun, SHEN Xiaoyong, et al. Scale-recurrent network for deep image deblurring[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 8174–8182.
    [15] ZHANG Jiawei, PAN Jinshan, REN J, et al. Dynamic scene deblurring using spatially variant recurrent neural networks[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 2521–2529.
    [16] GAO Hongyun, TAO Xin, SHEN Xiaoyong, et al. Dynamic scene deblurring with parameter selective sharing and nested skip connections[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 3843–3851.
    [17] KUPYN O, MARTYNIUK T, WU Junru, et al. DeblurGAN-v2: Deblurring (orders-of-magnitude) faster and better[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 8877–8886.
    [18] SUIN M, PUROHIT K, and RAJAGOPALAN A N. Spatially-attentive patch-hierarchical network for adaptive motion deblurring[C]. 2020 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 3603–3612.
    [19] LIU Qiaohong, SUN Liping, LING Chen, et al. Nonblind image deblurring based on Bi-composition decomposition by local smoothness and nonlocal self-similarity priors[J]. IEEE Access, 2019, 7: 63954–63971. doi: 10.1109/ACCESS.2019.2915314
    [20] LIU R W, YIN Wei, XIONG Shengwu, et al. L0-regularized hybrid gradient sparsity priors for robust single-image blind deblurring[C]. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Canada, 2018: 1348–1352.
    [21] LI Duo, HU Jie, WANG Changhu, et al. Involution: Inverting the inherence of convolution for visual recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition, online, 2021: 12321–12330.
    [22] BORJI A, CHENG Mingming, HOU Qibin, et al. Salient object detection: A survey[J]. Computational Visual Media, 2019, 5(2): 117–150. doi: 10.1007/s41095-019-0149-9
    [23] PANG Youwei, ZHAO Xiaoqi, ZHANG Lihe, et al. Multi-scale interactive network for salient object detection[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 9410–9419.
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出版历程
  • 收稿日期:  2021-04-13
  • 修回日期:  2021-11-14
  • 录用日期:  2021-11-14
  • 网络出版日期:  2021-12-22
  • 刊出日期:  2022-05-25

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