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基于残缺图像样本的生成对抗网络图像修复方法

李海燕 黄和富 郭磊 李海江 陈建华 李红松

李海燕, 黄和富, 郭磊, 等 . 基于残缺图像样本的生成对抗网络图像修复方法[J]. 北京航空航天大学学报, 2021, 47(10): 1949-1958. doi: 10.13700/j.bh.1001-5965.2020.0374
引用本文: 李海燕, 黄和富, 郭磊, 等 . 基于残缺图像样本的生成对抗网络图像修复方法[J]. 北京航空航天大学学报, 2021, 47(10): 1949-1958. doi: 10.13700/j.bh.1001-5965.2020.0374
LI Haiyan, HUANG Hefu, GUO Lei, et al. Image inpainting method based on incomplete image samples in generative adversarial network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(10): 1949-1958. doi: 10.13700/j.bh.1001-5965.2020.0374(in Chinese)
Citation: LI Haiyan, HUANG Hefu, GUO Lei, et al. Image inpainting method based on incomplete image samples in generative adversarial network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(10): 1949-1958. doi: 10.13700/j.bh.1001-5965.2020.0374(in Chinese)

基于残缺图像样本的生成对抗网络图像修复方法

doi: 10.13700/j.bh.1001-5965.2020.0374
基金项目: 

云南省万人计划"教学名师" 

云南省基础研究计划重点项目 202101AS070031

国家自然科学基金 61861045

详细信息
    通讯作者:

    郭磊, E-mail: lei_guo@ynu.edu.cn

  • 中图分类号: TP391.4

Image inpainting method based on incomplete image samples in generative adversarial network

Funds: 

"Famous Teacher" of Yunnan 10000 Talents Program 

Basic Research Key Project of Yunnan Province 202101AS070031

National Natural Science Foundation of China 61861045

More Information
  • 摘要:

    针对大面积图像修复缺失严重时,需要完整且高质量训练样本的问题,提出了一种将残缺或含噪图像样本作为训练集的双生成器深度卷积生成对抗网络(DGDCGAN)模型。构建两个生成器和一个鉴别器以解决单一生成器收敛慢的问题,用残缺图像样本作为训练集,通过交叉计算、搜索损失区域类似的图像信息作为训练生成模型的样本,收敛速度更快。鉴别器损失函数改进为输出的Wasserstein距离,使用自适应估计算法优化生成器损失函数和鉴别器损失函数的模型参数,最小化两两图像之间的总距离差,使用鉴别模型和修复图像总距离变化均方差最小化两个指标优化修复结果。在4个公开数据集上进行主客观实验,结果表明:所提方法能使用残缺图像样本作为训练集,有效实现大面积失真图像的修复,且收敛速度和修复效果优于现有图像修复方法。

     

  • 图 1  GAN模型训练

    Figure 1.  GAN model training

    图 2  DGDCGAN的网络结构

    Figure 2.  Network structure of DGDCGAN

    图 3  图像残缺分布

    Figure 3.  Image fragmentation distribution

    图 4  兔子训练样本流程

    Figure 4.  Rabbit training sample flow

    图 5  MNIST数字的残缺图像

    Figure 5.  Incomplete image of MNIST number

    图 6  熊的训练图像样本

    Figure 6.  Training samples of bears

    图 7  随迭代次数增加损失函数和修复效果

    Figure 7.  Results of loss function and inpainting as the number of iterations increases

    图 8  使用单个生成器时模型损失函数变化

    Figure 8.  Change of model loss values when using single generator

    图 9  CelebA数据集修复效果对比

    Figure 9.  Comparison of inpainting result of CelebA dataset

    图 10  Cifar10数据集修复效果对比

    Figure 10.  Comparison of inpainting result of Cifar10 dataset

    图 11  SVHN数据集中门牌号修复效果对比

    Figure 11.  Comparison of inpainting results of door number in SVHN dataset

    图 12  修复方法效果对比

    Figure 12.  Results comparison of inpainting methods

    表  1  不同方法的PSNR和SSIM对比

    Table  1.   PSNR/SSIM comparison of different methods

    方法 PSNR SSIM
    Car Black man Horse Car Black man Horse
    FMM[1] 24.49 22.25 23.03 0.79 0.73 0.75
    PM[4] 23.39 20.40 23.98 0.72 0.70 0.76
    SI[20] 31.12 40.39 38.35 0.97 0.96 0.91
    GAN_1[26] 24.54 25.57 22.79 0.91 0.91 0.83
    GAN_2[27] 30.38 36.22 37.98 0.94 0.91 0.95
    本文方法 32.19 41.06 38.59 0.99 0.99 0.97
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-30
  • 录用日期:  2020-10-25
  • 刊出日期:  2021-10-20

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