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基于递归残差网络的图像超分辨率重建

周登文 赵丽娟 段然 柴晓亮

周登文, 赵丽娟, 段然, 柴晓亮. 基于递归残差网络的图像超分辨率重建. 自动化学报, 2019, 45(6): 1157-1165. doi: 10.16383/j.aas.c180334
引用本文: 周登文, 赵丽娟, 段然, 柴晓亮. 基于递归残差网络的图像超分辨率重建. 自动化学报, 2019, 45(6): 1157-1165. doi: 10.16383/j.aas.c180334
ZHOU Deng-Wen, ZHAO Li-Juan, DUAN Ran, CHAI Xiao-Liang. Image Super-resolution Based on Recursive Residual Networks. ACTA AUTOMATICA SINICA, 2019, 45(6): 1157-1165. doi: 10.16383/j.aas.c180334
Citation: ZHOU Deng-Wen, ZHAO Li-Juan, DUAN Ran, CHAI Xiao-Liang. Image Super-resolution Based on Recursive Residual Networks. ACTA AUTOMATICA SINICA, 2019, 45(6): 1157-1165. doi: 10.16383/j.aas.c180334

基于递归残差网络的图像超分辨率重建

doi: 10.16383/j.aas.c180334
基金项目: 

中央高校基本科研业务费专项资金 2018ZD06

北京市自然科学基金 4162056

详细信息
    作者简介:

    赵丽娟   华北电力大学控制与计算机工程学院硕士研究生.2015年获得邢台学院数学与信息技术学院学士学位.主要研究方向为计算机视觉和深度学习.E-mail:zhaolj97@163.com

    段然  华北电力大学控制与计算机工程学院硕士研究生.2016年获得北京理工大学计算机学院学士学位.主要研究方向为计算机视觉和深度学习.E-mail:1162227075@ncepu.edu.cn

    柴晓亮  华北电力大学控制与计算机工程学院硕士研究生.2016年获得平顶山学院信息工程学院学士学位.主要研究方向为图像处理和图像超分辨率.E-mail:13051603700@163.com

    通讯作者:

    周登文  华北电力大学控制与计算机工程学院教授.主要研究方向为图像去噪, 图像去马赛克, 图像插值和图像超分辨率.本文通信作者.E-mail:zdw@ncepu.edu.cn

Image Super-resolution Based on Recursive Residual Networks

Funds: 

the Fundamental Research Funds for the Central Universities 2018ZD06

Beijing Natural Science Foundation 4162056

More Information
    Author Bio:

      Master student at the School of Control and Computer Engineering, North China Electric Power University. She received her bachelor degree from the School of Mathematics and Information Technology, Xingtai University in 2015. Her research interest covers computer vision and deep learning

      Master student at the School of Control and Computer Engineering, North China Electric Power University. She received her bachelor degree from the School of Computing, Beijing Institute of Technology in 2016. Her research interest covers computer vision and deep learning

     Master student at the School of Control and Computer Engineering, North China Electric Power University. He received his bachelor degree from the School of Information Engineering, Pingdingshan University in 2016. His research interest covers image processing and image super-resolution

    Corresponding author: ZHOU Deng-Wen    Professor at the School of Control and Computer Engineering, North China Electric Power University. His research interest covers image denoising, image demosaicing, image interpolation and image super-resolution. Corresponding author of this paper
  • 摘要: 深度卷积神经网络在单图像超分辨率重建方面取得了卓越成就,但其良好表现通常以巨大的参数数量为代价.本文提出一种简洁紧凑型递归残差网络结构,该网络通过局部残差学习减轻训练深层网络的困难,引入递归结构保证增加深度的同时控制模型参数数量,采用可调梯度裁剪方法防止产生梯度消失/梯度爆炸,使用反卷积层在网络末端直接上采样图像到超分辨率输出图像.基准测试表明,本文在重建出同等质量超分辨率图像的前提下,参数数量及计算复杂度分别仅为VDSR方法的1/10和1/(2n2).
    1)  本文责任编委 王亮
  • 图  1  残差块结构[18]

    Fig.  1  Residual block structure[18]

    图  2  网络结构示意图

    Fig.  2  Network structure diagram

    图  3  RRSR具体网络结构图

    Fig.  3  The specific network structure of RRSR

    图  4  递归块结构

    Fig.  4  Recursive block structure

    图  5  各种UC组合所构成网络的性能对比图

    Fig.  5  The performance of various networks at U and C combinations

    图  6  各种SISR方法的×3模型在Set5测试集上的平均PSNR值及参数数量

    Fig.  6  Average PSNR and number of parameters on the testset Set5 for scale factor ×3 of various SISR methods

    图  7  测试集BSD100中的"img_092"重建结果对比图

    Fig.  7  A comparison of the reconstruction results of "img_092" in the testset BSD100

    图  8  测试集Set5中的"butterfly"重建对比图

    Fig.  8  A comparison of the reconstruction results of "butterfly" in the testset Set5

    图  9  各种SISR方法的×4模型在测试集Set14上的平均运行时间及平均PSNR值[10]

    Fig.  9  Speed and average PSNR of various SISR methods on the Set14 with scale factor × 4[10]

    表  1  不同RRSR组件构成的$\times3$模型在Set5测试集上的平均PSNR值及参数量

    Table  1  Average PSNR and number of parameters when different RRSR components are turned on or off, for scale factor $\times3$ on testset Set5

    局部残差 递归结构 PSNR (dB) 参数数量($\times10^3$)
    × × 33.27 371
    × 33.58 371
    33.70 39
    下载: 导出CSV

    表  2  各种SISR方法的$\times2$, $\times3$和$\times4$模型在测试集Set5、Set14和BSD100上的平均PSNR值与SSIM值

    Table  2  Average PSNR/SSIMs of various SISR methords for scale factor $\times2$, $\times3$ and $\times4$ on Set5, Set14 and BSD100

    测试集 放大倍数 Bicubic PSNR/SSIM SelfEx PSNR/SSIM SRCNN PSNR/SSIM FSRCNN PSNR/SSIM VDSR PSNR/SSIM RRSR PSNR/SSIM
    Set5 ×2 33.66/0.9299 36.49/0.9537 36.66/0.9542 37.00/0.9558 37.53/0.9587 37.55/0.9588
    Set5 ×3 30.39/0.8682 32.58/0.9093 32.75/0.9090 33.16/0.9140 33.66/0.9213 33.70/0.9208
    Set5 ×4 28.42/0.8104 30.31/0.8619 30.48/0.8628 30.71/0.8657 31.35/0.8838 31.32/0.8836
    Set14 ×2 30.24/0.8688 32.22/0.9034 32.42/0.9063 32.64/0.9088 33.03/0.9124 33.04/0.9125
    Set14 ×3 27.55/0.7742 29.16/0.8196 29.28/0.8209 29.43/0.8242 29.77/0.8314 29.75/0.8307
    Set14 ×4 26.00/0.7027 27.40/0.7518 27.49/0.7503 27.60/0.7535 28.01/0.7674 28.00/0.7675
    BSD100 ×2 29.56/0.8431 31.18/0.8855 31.36/0.8879 31.51/0.8906 31.90/0.8960 31.91/0.8961
    BSD100 ×3 27.21/0.7385 28.29/0.7840 28.41/0.7863 28.52/0.7897 28.82/0.7976 28.78/0.7969
    BSD100 ×4 25.96/0.6675 26.84/0.7106 26.90/0.7101 26.97/0.7128 27.29/0.7251 27.25/0.7249
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-22
  • 录用日期:  2018-11-05
  • 刊出日期:  2019-06-20

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