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基于多尺度特征融合反投影网络的图像超分辨率重建

孙超文 陈晓

孙超文, 陈晓. 基于多尺度特征融合反投影网络的图像超分辨率重建. 自动化学报, 2021, 47(x): 1−12 doi: 10.16383/j.aas.c200714
引用本文: 孙超文, 陈晓. 基于多尺度特征融合反投影网络的图像超分辨率重建. 自动化学报, 2021, 47(x): 1−12 doi: 10.16383/j.aas.c200714
Sun Chao-Wen, Chen Xiao. Multiscale feature fusion back-projection network for image super-resolution. Acta Automatica Sinica, 2021, 47(x): 1−12 doi: 10.16383/j.aas.c200714
Citation: Sun Chao-Wen, Chen Xiao. Multiscale feature fusion back-projection network for image super-resolution. Acta Automatica Sinica, 2021, 47(x): 1−12 doi: 10.16383/j.aas.c200714

基于多尺度特征融合反投影网络的图像超分辨率重建

doi: 10.16383/j.aas.c200714
基金项目: 江苏省333高层次人才培养工程项目(2625); 江苏高校优势学科建设工程资助项目
详细信息
    作者简介:

    孙超文:南京信息工程大学电子与信息工程学院硕士研究生. 2018年获得南京理工大学紫金学院电子工程与光电技术系学士学位. 主要研究方向为深度学习和计算机视觉. E-mail: 20181219071@nuist.edu.cn

    陈晓:南京信息工程大学电子与信息工程学院教授. 主要研究方向为现代电子系统设计, 信号与信息处理, 图像处理, 超声成像等. 本文通讯作者. E-mail: chenxiao@nuist.edu.cn

Multiscale Feature Fusion Back-projection Network for Image Super-resolution

Funds: 333 high level personnel training project Jiangsu Province of China; the Priority Academic Program Development of Jiangsu Higher Education Institutions
More Information
    Author Bio:

    SUN Chao-Wen Master student at the School of Electronic and Information Engineering, Nanjing University of Information Science and Technology. She received her bachelor's degree in Electronic Engineering and Optoelectronic Technology from Zijin College, Nanjing University of Science and Technology in 2018. Her research interest covers deep learning and computer vision

    CHEN Xiao Professor in School of Electronic and Information Engineering, Nanjing University of Information Science and Technology. His research interests include modern electronic system design, signal and information processing, image processing, ultrasonic imaging. Corresponding author of this paper

  • 摘要: 针对现有图像超分辨率重建方法恢复图像高频细节能力较弱、特征利用率不足的问题, 提出了一种多尺度特征融合反投影网络用于图像超分辨率重建. 该网络首先在浅层特征提取层使用多尺度的卷积核提取不同维度的特征信息, 增强跨通道信息融合能力; 然后构建多尺度反投影模块通过递归学习执行特征映射, 提升网络的早期重建能力; 最后将局部残差反馈结合全局残差学习促进特征的传播和利用, 从而融合不同深度的特征信息进行图像重建. 对图像2到8倍超分辨率重建的实验结果表明, 本方法重建图像的质量在主观感受和客观评价指标上均优于现有图像超分辨率重建方法, 放大倍数大时重建性能相比更优秀.
  • 图  1  本文算法网络结构图

    Fig.  1  The structure of the proposed network

    图  2  8倍放大下对特征提取模块卷积核大小的分析

    Fig.  2  Analysis of kernel size in the feature extraction module on ×8 enlargement.

    图  3  主流重建算法在Set5数据集上对于×8 SR的平均PSNR和参数数量对比

    Fig.  3  Comparison of the average PSNR and the number of parameters of the mainstream reconstruction algorithm for ×8 SR on Set5

    图  4  Set5测试集下对不同网络的性能分析

    Fig.  4  Analysis of different networks under Set5

    图  5  在Set5上(woman)×8 SR的可视化结果

    Fig.  5  Visualized results of (woman) ×8 SR on Set5

    图  9  在Manga109上(TouyouKidan)×8 SR的可视化结果

    Fig.  9  Visualized results of ×8 SR on Manga109 (TouyouKidan)

    图  6  在Set14上(zebra)×8 SR的可视化结果

    Fig.  6  Visualized results of ×8 SR on Set14 (zebra)

    图  7  在BSD100上(210779)×8 SR的可视化结果

    Fig.  7  Visualized results of (210779) ×8 SR on BSD100

    图  8  在Urban100上(img005)×8 SR的可视化结果

    Fig.  8  Visualized results of (img005) ×8 SR on Urban100

    表  1  输入块大小、参数数量和网络超参数设置

    Table  1  The settings of input patch size, number of parameters and network hyperparameters

    放大倍数× 2× 3× 4× 8
    参数数量50162116490771816193916812691
    输入块尺寸60 × 6050 × 5040 × 4020 × 20
    特征提取模块特征提取层${f_{1 \times 1}}$: Conv (128,1,1,0); ${f_{{\rm{3}} \times {\rm{3}}}}$: Conv (128,3,1,1); ${f_{{\rm{5}} \times {\rm{5}}}}$: Conv (128,5,1,2)
    特征融合层Conv (128×3,1,1,1)
    特征映射模块支路1Conv1 (64,6,2,2)Conv1 (64,7,3,2)Conv1 (64,8,4,2)Conv1 (64,12,8,2)
    支路2Conv2 (64,8,2,3)Conv2 (64,9,3,3)Conv2 (64,10,4,3)Conv2 (64,14,8,3)
    重建模块Conv (64×7,3,1,1)
    递归次数7
    深度73
    注: Conv(C,K,S,P): C表示通道数, K表示卷积核大小, S表示步长, P表示填充.
    下载: 导出CSV

    表  2  对特征提取模块卷积核大小的分析

    Table  2  Analysis of the kernel size of the feature extraction module

    ScaleMethodSet5 PSNR/SSIMSet14 PSNR/SSIMBSD100 PSNR/SSIMUrban100 PSNR/SSIMManga109 PSNR/SSIM
    ×8Ours_13527.13/0.781925.02/0.644524.86/0.599222.59/0.623124.85/0.7885
    Ours_35727.09/0.780625.03/0.643724.86/0.598622.57/0.621924.78/0.7859
    下载: 导出CSV

    表  3  对多尺度投影单元的卷积核大小分析

    Table  3  Analysis of the kernel size of the multi-scale projection unit

    Scale(卷积核尺寸, 步长, 填充)PSNR (dB)
    支路1支路2Set5Set14BSD100Urban100Manga109
    ×8(8, 8, 0)(10, 8, 1)27.0024.9524.8222.4524.68
    (10, 8, 1)(12, 8, 2)27.0824.9924.8422.5324.77
    (12, 8, 2)(14, 8, 3)27.1325.0224.8622.5924.85
    下载: 导出CSV

    表  4  ×8模型在Set5和Set14测试集上的深度分析

    Table  4  The depth analysis of the ×8 model on Set5 and Set14 datasets

    递归次数网络层数参数数量PSNR (dB)
    Set5Set14
    1131680232326.5024.53
    3331680577926.9824.89
    4431680750727.0324.94
    5531680923527.0524.96
    6631681096327.0724.98
    7731681269127.1325.02
    8831681441927.1325.02
    下载: 导出CSV

    表  5  不同SR算法在×2、×3和×4上的定量评估

    Table  5  Quantitative comparison of different algorithms on ×2, ×3, and ×4

    ScaleMethodSet5 PSNR/SSIMSet14 PSNR/SSIMBSD100 PSNR/SSIMUrban100 PSNR/SSIMManga109 PSNR/SSIM
    × 21. Bicubic33.68/0.930430.24/0.869129.56/0.843526.88/0.840531.05/0.9350
    × 22. SRCNN36.66/0.954232.45/0.906731.36/0.887929.51/0.894635.72/0.9680
    × 23. ESPCN37.00/0.955932.75/0.909831.51/0.893929.87/0.906536.21/0.9694
    × 24. FSRCNN37.06/0.955432.76/0.907831.53/0.891229.88/0.902429.88/0.9024
    × 25. VDSR37.53/0.958733.05/0.912731.90/0.896030.77/0.914137.16/0.9740
    × 26. DRCN37.63/0.958833.06/0.912131.85/0.894230.76/0.913337.57/0.9730
    × 27. LapSRN37.52/0.959132.99/0.912431.80/0.894930.41/0.910137.53/0.9740
    × 28. DRRN37.74/0.959133.23/0.913632.05/0.897331.23/0.918837.92/0.9760
    × 29. DBPN-R64-737.57/0.958933.09/0.913231.83/0.895130.75/0.913337.65/0.9747
    × 210. IDN37.83/0.960033.30/0.914832.08/0.898531.27/0.919638.02/0.9749
    × 211. SRMDNF37.79/0.960133.32/0.915932.05/0.898531.33/0.920438.07/0.9761
    × 212. DRFN37.71/0.959533.29/0.914232.02/0.897931.08/0.917933.42/0.9123
    × 213. MRFN37.98/0.961133.41/0.915932.14/0.899731.45/0.922138.29/0.9759
    × 2Ours37.82/0.959933.35/0.915632.04/0.898031.49/0.921838.23/0.9762
    × 31. Bicubic30.40/0.868627.54/0.774127.21/0.738924.46/0.734926.95/0.8560
    × 32. SRCNN32.75/0.909029.29/0.821528.41/0.786326.24/0.799130.48/0.9120
    × 33. ESPCN33.02/0.913529.49/0.827128.50/0.793726.41/0.816130.79/0.9181
    × 34. FSRCNN33.20/0.914929.54/0.827728.55/0.794526.48/0.817530.98/0.9212
    × 35. VDSR33.66/0.921329.78/0.831828.83/0.797627.14/0.827932.01/0.9340
    × 36. DRCN33.82/0.922629.77/0.831428.80/0.796327.15/0.827732.31/0.9360
    × 37. LapSRN33.82/0.922729.79/0.832028.82/0.797327.07/0.827132.21/0.9350
    × 38. DRRN34.03/0.924429.96/0.834928.95/0.800427.53/0.837732.74/0.9390
    × 39. DBPN-R64-733.90/0.923629.99/0.835328.87/0.799127.35/0.833632.59/0.9373
    × 310. IDN34.11/0.925329.99/0.835428.95/0.801327.42/0.835932.69/0.9378
    × 311. SRMDNF34.12/0.925430.04/0.838228.97/0.802527.57/0.839833.00/0.9403
    × 312. DRFN34.01/0.923430.06/0.836628.93/0.801027.43/0.835930.59/0.8539
    × 313. MRFN34.21/0.926730.03/0.836328.99/0.802927.53/0.838932.82/0.9396
    × 3Ours34.31/0.926530.29/0.840829.05/0.803527.94/0.847233.37/0.9433
    × 41. Bicubic28.43/0.810926.00/0.702325.96/0.667823.14/0.657425.15/0.7890
    × 42. SRCNN30.48/0.862827.50/0.751326.9/0.710324.52/0.722627.66/0.8580
    × 43. ESPCN30.66/0.864627.71/0.756226.98/0.712424.60/0.736027.70/0.8560
    × 44. FSRCNN30.73/0.860127.71/0.748826.98/0.702924.62/0.727227.90/0.8517
    × 45. VDSR31.35/0.883828.02/0.767827.29/0.725225.18/0.752528.82/0.8860
    × 46. DRCN31.53/0.885428.03/0.767327.24/0.723325.14/0.751128.97/0.8860
    × 47. LapSRN31.54/0.886628.09/0.769427.32/0.726425.21/0.755329.09/0.8900
    × 48. DRRN31.68/0.888828.21/0.772027.38/0.728425.44/0.763829.46/0.8960
    × 49. DBPN-R64-731.92/0.891528.41/0.777027.42/0.730425.59/0.768129.92/0.9003
    × 410. IDN31.82/0.890328.25/0.773027.41/0.729725.41/0.7632
    × 411. SRMDNF31.96/0.892528.35/0.778727.49/0.733725.68/0.773130.09/0.9024
    × 412. DRFN31.55/0.886128.30/0.773727.39/0.729325.45/0.762928.99/0.8106
    × 413. MRFN31.90/0.891628.31/0.774627.43/0.730925.46/0.765429.57/0.8962
    × 4Ours32.31/0.896328.71/0.784327.66/0.738326.30/0.792230.84/0.9126
    下载: 导出CSV

    表  6  不同SR算法在×8上的定量评估

    Table  6  Quantitative comparison of different algorithms on ×8

    ScaleMethodSet5 PSNR/SSIMSet14 PSNR/SSIMBSD100 PSNR/SSIMUrban100 PSNR/SSIMManga109 PSNR/SSIM
    × 81. Bicubic24.40/0.658023.10/0.566023.67/0.548020.74/0.516021.47/0.6500
    × 82. SRCNN25.33/0.690023.76/0.591024.13/0.566021.29/0.544022.46/0.6950
    × 83. ESPCN25.75/0.673824.21/0.510924.37/0.527721.59/0.542022.83/0.6715
    × 84. FSRCNN25.42/0.644023.94/0.548224.21/0.511221.32/0.509022.39/0.6357
    × 85. VDSR25.93/0.724024.26/0.614024.49/0.583021.70/0.571023.16/0.7250
    × 86. LapSRN26.15/0.738024.35/0.620024.54/0.586021.81/0.581023.39/0.7350
    × 87. DRFN26.22/0.740024.57/0.625024.60/0.5870
    × 88. MSRN26.59/0.725424.88/0.596124.70/0.541022.37/0.597724.28/0.7517
    × 89. DBPN-R64-726.82/0.770024.77/0.634624.72/0.592822.22/0.603324.19/0.7664
    × 810. EDSR26.96/0.776224.91/0.642024.81/0.598522.51/0.622124.69/0.7841
    × 8Ours27.13/0.781925.02/0.644524.86/0.599222.59/0.623124.85/0.7885
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-02
  • 录用日期:  2021-02-09
  • 网络出版日期:  2021-03-17

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