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基于混合生成对抗网络的多视角图像生成算法

卫星 李佳 孙晓 刘邵凡 陆阳

卫星, 李佳, 孙晓, 刘邵凡, 陆阳. 基于混合生成对抗网络的多视角图像生成算法. 自动化学报, 2021, 47(11): 2623−2636 doi: 10.16383/j.aas.c190743
引用本文: 卫星, 李佳, 孙晓, 刘邵凡, 陆阳. 基于混合生成对抗网络的多视角图像生成算法. 自动化学报, 2021, 47(11): 2623−2636 doi: 10.16383/j.aas.c190743
Wei Xing, Li Jia, Sun Xiao, Liu Shao-Fan, Lu Yang. Cross-view image generation via mixture generative adversarial network. Acta Automatica Sinica, 2021, 47(11): 2623−2636 doi: 10.16383/j.aas.c190743
Citation: Wei Xing, Li Jia, Sun Xiao, Liu Shao-Fan, Lu Yang. Cross-view image generation via mixture generative adversarial network. Acta Automatica Sinica, 2021, 47(11): 2623−2636 doi: 10.16383/j.aas.c190743

基于混合生成对抗网络的多视角图像生成算法

doi: 10.16383/j.aas.c190743
基金项目: 2020年安徽省自然科学基金联合基金(2008085UD08), 安徽省重点研发计划项目(201904d08020008, 202004a05020004), 合肥工业大学智能制造技术研究院智能网联及新能源汽车技术成果转化及产业化项目(IMIWL2019003, IMIDC2019002)资助
详细信息
    作者简介:

    卫星:合肥工业大学副教授. 2009年于中国科技大学获得博士学位. 主要研究方向为深度学习与物联网工程, 无人驾驶解决方案. E-mail: weixing@hfut.edu.cn

    李佳:合肥工业大学计算机与信息学院硕士研究生. 主要研究方向为自然语言处理, 情感对话生成. E-mail: lijiajia@mail.hfut.edu.cn

    孙晓:博士, 合肥工业大学计算机与信息学院情感计算研究所副教授. 主要研究方向为情感计算, 自然语言处理, 机器学习与人机交互, 本文通信作者. E-mail: sunx@hfut.edu.cn

    刘邵凡:合肥工业大学硕士研究生. 2018年于合肥工业大学获得学士学位. 主要研究方向为目标检测和领域自适应. E-mail: frank-uzi@hotmail.com

    陆阳:合肥工业大学教授. 2002年于合肥工业大学获得博士学位. 主要研究方向为物联网工程和分布式控制系统. E-mail: luyang.hf@126.com

Cross-view Image Generation via Mixture Generative Adversarial Network

Funds: Supported by Joint Fund of Natural Science Foundation of Anhui Province in 2020 (2008085UD08), Anhui Provincial Key Research and Development Program (201904d08020008, 202004a05020004), and Intelligent Networking and New Energy Vehicle Special Project of Intelligent Manufacturing Institute of Hefei University of Technology (IMIWL2019003, IMIDC2019002)
More Information
    Author Bio:

    WEI Xing Associate professor at Hefei University of Technology. He received his Ph.D. degree from University of Science and Technology of China in 2009. His research interest covers deep learning and internet of things engineering, and driverless solutions

    LI Jia Master student at the School of Computer and Information, Hefei University of Technology. His research interest covers natural language processing and emotional conversation generation

    SUN Xiao Ph.D., associate professor at the Institute of Affective Computing, Hefei University of Technology. His research interest covers affective computing, natural language processing, machine learning and human-machine interaction. Corresponding author of this paper

    LIU Shao-Fan Master student at Hefei University of Technology. He received his bachelor degree from Hefei University of Technology in 2018. His research interest covers object detection and domain adaptation

    LU Yang Professor at Hefei University of Technology. He received his Ph.D. degree from Hefei University of Technology in 2002. His research interest covers IOT (internet of things) engineering and distributed control system

  • 摘要: 多视角图像生成即基于某个视角图像生成其他多个视角图像, 是多视角展示和虚拟现实目标建模等领域的基本问题, 已引起研究人员的广泛关注. 近年来, 生成对抗网络(Generative adversarial network, GAN)在多视角图像生成任务上取得了不错的成绩, 但目前的主流方法局限于固定领域, 很难迁移至其他场景, 且生成的图像存在模糊、失真等弊病. 为此本文提出了一种基于混合对抗生成网络的多视角图像生成模型ViewGAN, 它包括多个生成器和一个多类别判别器, 可灵活迁移至多视角生成的多个场景. 在ViewGAN中, 多个生成器被同时训练, 旨在生成不同视角的图像. 此外, 本文提出了一种基于蒙特卡洛搜索的惩罚机制来促使每个生成器生成高质量的图像, 使得每个生成器更专注于指定视角图像的生成. 在DeepFashion, Dayton, ICG Lab6数据集上的大量实验证明: 我们的模型在Inception score和Top-k accuracy上的性能优于目前的主流模型, 并且在结构相似性(Structural similarity, SSIM)上的分数提升了32.29%, 峰值信噪比(Peak signal-to-noise ratio, PSNR)分数提升了14.32%, SD (Sharpness difference)分数提升了10.18%.
    1)  收稿日期 2019-10-25 录用日期 2020-02-23 Manuscript received October 25, 2019; accepted February 23,2020 2020年安徽省自然科学基金联合基金(2008085UD08), 安徽省重点研发计划项目(201904d08020008, 202004a05020004), 合肥工业大学智能制造技术研究院智能网联及新能源汽车技术成果转化及产业化项目(IMIWL2019003, IMIDC2019002)资助 Supported by Joint Fund of Natural Science Foundation of Anhui Province in 2020 (2008085UD08), Anhui Provincial Key Research and Development Program (201904d08020008, 202004a05020004), and Intelligent Networking and New Energy Vehicle Special Project of Intelligent Manufacturing Institute of Hefei University of Technology (IMIWL2019003, IMIDC2019002) 本文责任编委 吴建鑫 Recommended by Associate Editor WU Jian-Xin 1. 合肥工业大学计算机与信息学院 合肥 230601
    2)  1. School of Computer and Information, Hefei University of Technology, Hefei 230601
  • 图  1  本文模型ViewGAN在DeepFashion、Dayton和ICG Lab6数据集上的测试样例

    Fig.  1  Examples of ViewGAN on three datasets, i.e., DeepFashion, Dayton and ICG Lab6

    图  2  ViewGAN模型的整体框架

    Fig.  2  The framework of ViewGAN

    图  3  生成器$ \left(G_{j}\right) $的整体框架

    Fig.  3  The framework of the generator $ G_j $

    图  4  各模型在DeepFashion数据集上的测试样例

    Fig.  4  Results generated by different models on DeepFashion dataset

    图  5  各模型在Dayton数据集上的测试样例

    Fig.  5  Results generated by different models on Dayton dataset

    图  6  各模型在ICG Lab6数据集上的测试样例

    Fig.  6  Results generated by different models on ICG Lab6 dataset

    图  7  ViewGAN生成图像的可视化过程((a)输入图像; (b)粗粒度模块合成的低分辨率目标图像;(c)蒙特卡洛搜索的结果; (d)细粒度模块合成的高分辨率目标图像)

    Fig.  7  Visualization of the process of ViewGAN generating images ((a) The input image; (b) The LR image generated by coarse image module; (c) Intermediate results generated by Monte Carlo search module; (d) The HR image generated by fine image module)

    表  1  生成器网络结构

    Table  1  Generator network architecture

    网络 层次 输入 输出
    Down-Sample CONV(N64, K4×4, S1, P3)-BN-Leaky Relu (256, 256, 3) (256, 256, 64)
    CONV(N128, K4×4, S2, P1)-BN-Leaky Relu (256, 256, 64) (128, 128, 128)
    CONV(N256, K4×4, S2, P1)-BN-Leaky Relu (128, 128, 128) (64, 64, 256)
    CONV(N512, K4×4, S2, P1)-BN-Leaky Relu (64, 64, 256) (32, 32, 512)
    Residual Block CONV(N512, K4×4, S1, P1)-BN-Leaky Relu (32, 32, 512) (32, 32, 512)
    CONV(N512, K4×4, S1, P1)-BN-Leaky Relu (32, 32, 512) (32, 32, 512)
    CONV(N512, K4×4, S1, P1)-BN-Leaky Relu (32, 32, 512) (32, 32, 512)
    CONV(N512, K4×4, S1, P1)-BN-Leaky Relu (32, 32, 512) (32, 32, 512)
    CONV(N512, K4×4, S1, P1)-BN-Leaky Relu (32, 32, 512) (32, 32, 512)
    Up-Sample DECONV(N256, K4×4, S2, P1)-BN-Leaky Relu (32, 32, 512) (64, 64, 256)
    DECONV(N128, K4×4, S2, P1)-BN-Leaky Relu (64, 64, 256) (128, 128, 128)
    DECONV(N64, K4×4, S1, P3)-BN-Leaky Relu (128, 128, 128) (256, 256, 64)
    CONV(N3, K4×4, S1, P3)-BN-Leaky Relu (256, 256, 64) (256, 256,3)
    下载: 导出CSV

    表  2  判别器网络结构

    Table  2  Discriminator network architecture

    网络 层次 输入 输出
    Input Layer CONV(N64, K3×3, S1, P1)-Leaky Relu (256, 256, 3) (256, 256, 64)
    CONV BLOCK (256, 256, 64) (256, 256, 64)
    CONV BLOCK (256, 256, 64) (128, 128 128)
    CONV BLOCK (128, 128 128) (64, 64 256)
    Inner Layer HIDDEN LAYER (64, 64 256) (32, 32 512)
    HIDDEN LAYER (32, 32 512) (32, 32 64)
    DECONV BLOCK (32, 32 64) (64, 64 64)
    DECONV BLOCK (64, 64 64) (128, 128, 64)
    DECONV BLOCK (128, 128, 64) (256, 256, 64)
    Output layer CONV(N64, K3×3, S1, P1)-Leaky Relu (256, 256, 64) (256, 256, 3)
    CONV(N64, K3×3, S1, P1)-Leaky Relu
    CONV(N3, K3×3, S1, P1)-Tanh
    下载: 导出CSV

    表  3  各模型Inception score统计表, 该指标越高表明模型性能越好

    Table  3  Inception score of different models (For this metric, higher is better)

    模型 DeepFashion Dayton ICG Lab6
    all classes Top-1 class Top-5 class all classes Top-1 class Top-5 class all classes Top-1 class Top-5 class
    Pix2Pix 3.37 2.23 3.44 2.85 1.93 2.91 2.54 1.69 2.49
    X-Fork 3.45 2.57 3.56 3.07 2.24 3.09 4.65 2.14 3.85
    X-Seq 3.83 2.68 4.02 2.74 2.13 2.77 4.51 2.05 3.66
    VariGAN 3.79 2.71 3.94 2.77 2.19 2.79 4.66 2.15 3.72
    SelectionGAN 3.81 2.72 3.91 3.06 2.27 3.13 5.64 2.52 4.77
    ViewGAN 4.10 2.95 4.32 3.18 2.27 3.36 5.92 2.71 4.91
    Real Data 4.88 3.31 5.00 3.83 2.58 3.92 6.46 2.86 5.47
    下载: 导出CSV

    表  4  各模型Top-k预测准确率统计表, 该指标越高表明模型性能越好

    Table  4  Accuracies of different models (For this metric, higher is better)

    模型 DeepFashion Dayton ICG Lab6
    Top-1 class Top-5 class Top-1 class Top-5 class Top-1 class Top-5 class
    Pix2Pix 7.34 9.28 25.79 32.68 6.80 9.15 23.55 27.00 1.33 1.62 5.43 6.79
    X-Fork 20.68 31.35 50.45 64.12 30.00 48.68 61.57 78.84 5.94 10.36 20.83 30.45
    X-Seq 16.03 24.31 42.97 54.52 30.16 49.85 62.59 80.70 4.87 8.94 17.13 24.47
    VariGAN 25.67 31.43 55.52 63.70 32.21 52.69 67.95 84.31 10.44 20.49 33.45 41.62
    SelectionGAN 41.57 64.55 72.30 88.65 42.11 68.12 77.74 92.89 28.35 54.67 62.91 76.44
    ViewGAN 65.73 95.77 91.65 98.21 69.39 89.88 93.47 98.78 58.97 83.20 88.74 93.25
    下载: 导出CSV

    表  5  各模型SSIM, PSNR, SD和速度统计表, 其中FPS表示测试时每秒处理的图像数量,所有指标得分越高表明模型性能越好

    Table  5  SSIM, PSNR, SD of different models. FPS is the number of images processed per second during testing(For all metrics, higher is better)

    模型 DeepFashion Dayton ICG Lab6 速度 (帧/s)
    SSIM PSNR SD SSIM PSNR SD SSIM PSNR SD
    Pix2Pix 0.39 17.67 18.55 0.42 17.63 19.28 0.23 15.71 16.59 166±5
    X-Fork 0.45 19.07 18.67 0.50 19.89 19.45 0.27 16.38 17.35 87±7
    X-Seq 0.42 18.82 18.44 0.50 20.28 19.53 0.28 16.38 17.27 75±3
    VariGAN 0.57 20.14 18.79 0.52 21.57 19.77 0.45 17.58 17.89 70±5
    SelectionGAN 0.53 23.15 19.64 0.59 23.89 20.02 0.61 26.67 19.76 66±6
    ViewGAN 0.70 26.47 21.63 0.74 25.97 21.37 0.80 28.61 21.77 62±2
    下载: 导出CSV

    表  6  最小数据量实验结果

    Table  6  Minimum training data experimental results

    数据量 (幅) SSIM PSNR SD
    6000 (100%) 0.70 26.47 21.63
    5400 (90%) 0.68 26.08 20.95
    4800 (80%) 0.66 24.97 20.31
    4200 (70%) 0.59 23.68 20.00
    3600 (60%) 0.51 21.90 18.89
    下载: 导出CSV

    表  7  消融分析实验结果

    Table  7  Ablations study of the proposed ViewGAN

    模型 结构 SSIM PSNR SD
    A Pix2Pix 0.46 19.66 18.89
    B A+由粗到精生成方法 0.53 22.90 19.31
    C B+混合生成对抗网络 0.60 23.77 20.03
    D C+类内损失 0.60 23.80 20.11
    E D+惩罚机制 0.70 26.47 21.63
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-10-25
  • 录用日期:  2020-02-23
  • 网络出版日期:  2021-10-21
  • 刊出日期:  2021-11-18

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