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强边缘提取网络用于非均匀运动模糊图像盲复原

黄彦宁 李伟红 崔金凯 龚卫国

黄彦宁, 李伟红, 崔金凯, 龚卫国. 强边缘提取网络用于非均匀运动模糊图像盲复原. 自动化学报, 2021, 47(11): 1−17 doi: 10.16383/j.aas.c190654
引用本文: 黄彦宁, 李伟红, 崔金凯, 龚卫国. 强边缘提取网络用于非均匀运动模糊图像盲复原. 自动化学报, 2021, 47(11): 1−17 doi: 10.16383/j.aas.c190654
Huang Yan-Ning, Li Wei-Hong, Cui Jin-Kai, Gong Wei-Guo. Strong edge extraction network for non-uniform blind motion image deblurring. Acta Automatica Sinica, 2021, 47(11): 1−17 doi: 10.16383/j.aas.c190654
Citation: Huang Yan-Ning, Li Wei-Hong, Cui Jin-Kai, Gong Wei-Guo. Strong edge extraction network for non-uniform blind motion image deblurring. Acta Automatica Sinica, 2021, 47(11): 1−17 doi: 10.16383/j.aas.c190654

强边缘提取网络用于非均匀运动模糊图像盲复原

doi: 10.16383/j.aas.c190654
基金项目: 国家科技惠民计划项目(2013GS500303), 广西科学研究与技术开发计划项目(桂科AA17129002)资助
详细信息
    作者简介:

    黄彦宁:重庆大学光电工程学院硕士研究生. 主要研究方向为图像处理技术. E-mail: 20122858@cqu.edu.cn

    李伟红:重庆大学光电工程学院教授. 主要研究方向为图像处理技术, 模式识别. 本文通信作者.E-mail: weihongli@cqu.edu.cn

    崔金凯:重庆大学光电工程学院博士研究生. 主要研究方向为图像处理技术. E-mail: jinkaicui@cqu.edu.cn

    龚卫国:重庆大学光电工程学院教授. 主要研究方向为图像处理技术.E-mail: wggong@cqu.edu.cn

Strong Edge Extraction Network for Non-uniform Blind Motion Image Deblurring

Funds: Supported by the National Science and Technolog Program for Public Wellbeing, China (2013GS500303), the Key Projects of Science and Technology Agency of Guangxi Province, China (Guike AA17129002)
More Information
    Author Bio:

    HUANG Yan-Ning Master student at the College of Optoelectronic Engineering, Chongqing University. His main research interest is image processing technology

    LI Wei-Hong Professor at the College of Optoelectronic Engineering, Chongqing University. Her research interest covers image processing technology and pattern recognition. Corresponding author of this paper

    CUI Jin-Kai Ph. D. candidate at the College of Optoelectronic Engineering, Chongqing University. His main research interest is image processing technology

    GONG Wei-Guo Professor at the College of Optoelectronic Engineering, Chongqing University. His main research interest is image processing technology

  • 摘要: 基于深度学习的非均匀运动图像去模糊方法已经获得了较好的效果. 然而, 现有的方法通常存在对边缘恢复不清晰的问题. 因此, 本文提出一种强边缘提取网络(Strong-edge extraction network, SEEN), 用于提取非均匀运动模糊图像的强边缘以提高图像边缘复原质量. 设计的强边缘提取网络由两个子网络SEEN-1和SEEN-2组成, SEEN-1实现双边滤波器的功能, 用于提取滤除了细节信息后的图像边缘. SEEN-2实现L0平滑滤波器的功能, 用于提取模糊图像的强边缘. 本文还将对应网络层提取的强边缘特征图与模糊特征图叠加, 进一步利用强边缘特征. 最后, 本文在GoPro数据集上进行了验证实验, 结果表明: 本文提出的网络可以较好地提取非均匀运动模糊图像的强边缘, 复原图像在客观和主观上都可以达到较好的效果.
  • 图  1  均匀运动模糊图像和非均匀运动模糊图像及其模糊核示意图

    Fig.  1  Uniform and non-uniform motion blurry image and their blur kernel diagram

    图  2  提出的非均匀运动模糊图像复原网络结构图

    Fig.  2  Proposed network structure for the non-uniform motion blurry image deblurring

    图  3  模糊图像强边缘提取原理示意图

    Fig.  3  Diagram of strong edge extraction of blurred image

    图  4  强边缘提取网络结构示意图

    Fig.  4  Structure diagram of strong edge extraction network

    图  5  本文提出的双方向融合梯度计算方法示意图

    Fig.  5  Diagram of gradient calculation method of two-directions fusion proposed in this paper

    图  6  改进残差模块

    Fig.  6  Improvement of residual block

    图  7  不同大小滤波器的感受野

    Fig.  7  Receptive field of different size filters

    图  8  数据集示例图像

    Fig.  8  Sample images of dataset

    图  9  有无强边缘提取网络的非均匀模糊图像复原结果比较实例 ((a) 模糊图像; (b) 模糊图像梯度图;(c) SEEN-1 输出; (d) SEEN-2 输出 (强边缘); (e) 无强边缘提取网络的复原结果;(f) 有强边缘提取网络的复原结果; (g) 清晰图像)

    Fig.  9  Comparison of restoration results of non-uniform motion blurry image with or without edge extraction network ((a) Blurry image; (b) Gradient of blurry image; (c) Output of SEEN-1; (d) Edge of SEEN-2 (Strong edge); (e) Restoration results of network without strong edge extraction network; (f) Restoration results of network with strong edge extraction network; (g) Clear image)

    图  10  强边缘提取网络中间结果分析

    Fig.  10  The intermediate results analysis of strong edge extraction network

    图  11  强边缘提取网络效果分析

    ((i)模糊图像小块(30×30); (ii)模糊图像小块边缘(30×30); (iii) SEEN-1输出;(iv) SEEN-2输出; (v)灰度分布图; (vi)列灰度值加和曲线图)

    Fig.  11  Effect analysis of strong edge extraction network

    ((i) Blurry image patch (30×30); (ii) Edge of blurry image patch (30×30); (iii) Output of SEEN-1; (iv) Output of SEEN-2; (v) Gray value distribution; (vi) Column gray value addition)

    12  对比实验结果

    12  Results of comparative experiments

    图  13  对比实验图像的PSNR和SSIM指标值柱状图

    Fig.  13  Histogram of PSNR and SSIM values of comparative experimental images

    表  1  交叉特征提取残差模块有效性验证实验结果(GoPro数据集)

    Table  1  Validation experiment results of cross-resnet block (GoPro dataset)

    评价指标残差模块交叉特征提取残差模块
    PSNR29.980030.2227
    SSIM0.88920.8944
    下载: 导出CSV

    表  2  对比实验图像的PSNR值比较

    Table  2  PSNR value comparison of comparative experimental images

    图像文献 [4]文献 [7]文献 [10]文献 [13]文献 [21]本文方法
    (i)26.363027.824829.819427.247327.258830.8864
    (ii)25.909825.336826.054022.524424.901127.0760
    (iii)22.527025.727929.416425.887726.782328.6511
    (iv)24.586827.418729.654825.738827.483730.0529
    下载: 导出CSV

    表  3  对比实验图像的SSIM值比较

    Table  3  SSIM value comparison of comparative experimental images

    图像文献 [4]文献 [7]文献 [10]文献 [13]文献 [21]本文方法
    (i)0.80310.84720.88210.78580.82900.8967
    (ii)0.79560.75930.82330.60340.74240.8335
    (iii)0.65020.76580.87380.71320.79280.8497
    (iv)0.74700.81700.87410.71250.82180.8764
    下载: 导出CSV

    表  4  复原图像的平均PSNR值和平均SSIM值(GoPro数据集)

    Table  4  Average value of PSNR and SSIM of restored images (GoPro dataset)

    评价指标文献 [4]文献 [7]文献 [10]文献 [13]文献 [21]文献 [22]文献 [23]本文方法
    PSNR(dB)24.503426.398030.100027.464824.529425.920029.550030.2227
    SSIM0.76210.82770.91370.80930.77610.78100.93400.8944
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-09-12
  • 录用日期:  2020-05-07
  • 网络出版日期:  2021-09-07

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