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基于像素对比学习的图像超分辨率算法

周登文 刘子涵 刘玉铠

周登文, 刘子涵, 刘玉铠. 基于像素对比学习的图像超分辨率算法. 自动化学报, 2024, 50(1): 181−193 doi: 10.16383/j.aas.c230395
引用本文: 周登文, 刘子涵, 刘玉铠. 基于像素对比学习的图像超分辨率算法. 自动化学报, 2024, 50(1): 181−193 doi: 10.16383/j.aas.c230395
Zhou Deng-Wen, Liu Zi-Han, Liu Yu-Kai. Pixel-wise contrastive learning for single image super-resolution. Acta Automatica Sinica, 2024, 50(1): 181−193 doi: 10.16383/j.aas.c230395
Citation: Zhou Deng-Wen, Liu Zi-Han, Liu Yu-Kai. Pixel-wise contrastive learning for single image super-resolution. Acta Automatica Sinica, 2024, 50(1): 181−193 doi: 10.16383/j.aas.c230395

基于像素对比学习的图像超分辨率算法

doi: 10.16383/j.aas.c230395
详细信息
    作者简介:

    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

    刘子涵:华北电力大学控制与计算机工程学院硕士研究生. 主要研究方向为计算机视觉, 深度学习. E-mail: 120212227102@ncepu.edu.cn

    刘玉铠:华北电力大学控制与计算机工程学院硕士研究生. 主要研究方向为计算机视觉, 深度学习. E-mail: liuyk@ncepu.edu.cn

Pixel-wise Contrastive Learning for Single Image Super-resolution

More Information
    Author Bio:

    ZHOU Deng-Wen Professor at the School of Control and Compu-ter Engineering, North China Electric Power University. His research interest covers image denoising, image demosaic, image interpolation, and image super-resolution. Corresponding author of this paper

    LIU Zi-Han Master student at the School of Control and Compu-ter Engineering, North China Electric Power University. His research interest covers computer vision and deep learning

    LIU Yu-Kai Master student at the School of Control and Compu-ter Engineering, North China Electric Power University. His research interest covers computer vision and deep learning

  • 摘要: 目前, 深度卷积神经网络(Convolutional neural network, CNN)已主导了单图像超分辨率(Single image super-resolution, SISR)技术的研究, 并取得了很大进展. 但是, SISR仍是一个开放性问题, 重建的超分辨率(Super-resolution, SR)图像往往会出现模糊、纹理细节丢失和失真等问题. 提出一个新的逐像素对比损失, 在一个局部区域中, 使SR图像的像素尽可能靠近对应的原高分辨率(High-resolution, HR)图像的像素, 并远离局部区域中的其他像素, 可改进SR图像的保真度和视觉质量. 提出一个组合对比损失的渐进残差特征融合网络(Progressive residual feature fusion network, PRFFN). 主要贡献有: 1)提出一个通用的基于对比学习的逐像素损失函数, 能够改进SR图像的保真度和视觉质量; 2)提出一个轻量的多尺度残差通道注意力块(Multi-scale residual channel attention block, MRCAB), 可以更好地提取和利用多尺度特征信息; 3)提出一个空间注意力融合块(Spatial attention fuse block, SAFB), 可以更好地利用邻近空间特征的相关性. 实验结果表明, PRFFN显著优于其他代表性方法.
  • 图  1  不同损失及其组合的PSNR/SSIM和视觉效果

    Fig.  1  PSNR/SSIM and visual effects for different losses and their combinations

    图  2  在Set14数据集上, 不同SISR方法2倍SR结果的平均PSNR值和参数量

    Fig.  2  Average PSNRs and parameter counts for 2 times SR models for each state-of-the-art SISR method on the Set14 dataset

    图  3  网络架构细节

    Fig.  3  Network architecture details

    图  4  多尺度残差通道注意力块

    Fig.  4  Multi-scale residual channel attention block

    图  5  空间注意力融合块

    Fig.  5  Spatial attention fusion block

    图  6  像素级对比损失

    Fig.  6  Pixel-wise contrastive loss

    图  7  Urban100数据集中, SwinIR-light使用不同损失函数, img004图像的3倍SR结果

    Fig.  7  The 3 times SR results of SwinIR-light using different losses on the img004 image inthe Urban100 data set

    图  8  2倍SR的视觉效果比较

    Fig.  8  Visual comparison for 2 times SR

    图  9  3倍SR的视觉效果比较

    Fig.  9  Visual comparison for 3 times SR

    图  10  4倍SR的视觉效果比较

    Fig.  10  Visual comparison for 4 times SR

    表  1  DIV2K_val5验证集上, 不同模型, 3倍SR的平均PSNR和参数量

    Table  1  The average PSNRs and parameter counts of 3 times SR for different models on the DIV2K_val5 validation data set

    模型$L_{1}$$L_{cntr}$参数量(K)PSNR (dB)
    PRFFN0$\checkmark$297532.259
    PRFFN1$\checkmark$322232.307
    PRFFN2$\checkmark$316732.342
    PRFFN3$\checkmark$316732.364
    PRFFN$\checkmark$$\checkmark$316732.451
    下载: 导出CSV

    表  2  DIV2K_val5验证集上, 不同损失函数及其组合, 3倍SR的平均PSNR和LPIPS结果

    Table  2  The average PSNRs and LPIPSs of 3 times SR for different losses and their combinations on the DIV2K_val5 validation data set

    $L_{1}$$L_{perc}$$L_{CSD}$$L_{cntr}$PSNR (dB)LPIPS
    $\checkmark$32.3640.0978
    $\checkmark$$\checkmark$32.4510.0969
    $\checkmark$$\checkmark$32.2360.0672
    $\checkmark$$\checkmark$$\checkmark$32.3050.0656
    $\checkmark$$\checkmark$32.3870.0624
    $\checkmark$$\checkmark$$\checkmark$32.4320.0613
    下载: 导出CSV

    表  3  DIV2K_val5验证集上, 不同$ \lambda_{C} $, 3倍SR的平均PSNR结果

    Table  3  The average PSNRs of 3 times SR for different $ \lambda_{C} $on the DIV2K_val5 validation data set

    $\lambda_{C}$PSNR (dB)
    $10^{-2}$32.386
    $10^{-1}$32.451
    $1$32.381
    $10$32.372
    下载: 导出CSV

    表  4  DIV2K_val5验证集上, 不同模型包含与不包含$ L_{cntr} $损失, 3倍SR的平均PSNR和SSIM结果

    Table  4  The average PSNRs and SSIMs of 3 times SR for different models with and without $ L_{cntr} $ loss on the DIV2K_val5 validation data set

    模型$L_{cntr}$PSNR (dB)SSIM
    EDSR32.2730.9057
    $ \checkmark$32.334 ($\uparrow 0.061$)0.9067 ($\uparrow 0.0010$)
    SwinIR-light32.4420.9062
    $ \checkmark$32.489 ($\uparrow 0.047$)0.9069 ($\uparrow 0.0007$)
    RCAN32.5640.9088
    $ \checkmark$32.628 ($\uparrow 0.064$)0.9096 ($\uparrow 0.0008$)
    下载: 导出CSV

    表  5  DIV2K_val5验证集上, 不同大小局部区域, 3倍SR的平均PSNR结果

    Table  5  The average PSNRs of 3 times SR for different size local regions on the DIV2K_val5 validation data set

    $S$PSNR (dB)
    $16$32.425
    $64$32.451
    $256$32.458
    下载: 导出CSV

    表  6  3倍SR训练10个迭代周期, 训练占用的内存和使用的训练时间

    Table  6  For 3 times SR, 10 epochs, comparing the memory and time used by training

    $L_{cntr}$$S$内存(MB)时间(s)
    41261962
    $\checkmark$1648362081
    $\checkmark$6452162219
    $\checkmark$25675412893
    下载: 导出CSV

    表  7  DIV2K_val5验证集上, MRCAB不同分支和不同扩张率组合, 3倍SR的平均PSNR结果

    Table  7  The average PSNRs of 3 times SR for the different branches of MRCAB with different dilation rate combinations on the DIV2K_val5 validation data set

    不同的扩张率卷积组合PSNR (dB)
    $1$32.375
    $1, 2$32.370
    $1, 2, 3$32.392
    $1, 2, 4$32.451
    $1, 2, 5$32.415
    下载: 导出CSV

    表  8  5个标准测试数据集上, 不同SISR方法的2倍、3倍和4倍SR的平均PSNR和SSIM结果

    Table  8  The average PSNRs and SSIMs of 2 times, 3 times, and 4 times SR for different SISR methods on five standard test data sets

    放大倍数方法参数量(K)计算量(G)推理时间(ms)Set5 PSNR/SSIMSet14 PSNR/SSIMB100 PSNR/SSIMUrban100 PSNR/SSIMManga109 PSNR/SSIM
    2FSRCNN136.02637.00/0.955832.63/0.908831.53/0.892029.88/0.902036.67/0.9694
    SMSR985224.153638.00/0.960133.64/0.917932.17/0.899032.19/0.928438.76/0.9771
    ACAN8002108.038.10/0.960833.60/0.917732.21/0.900132.29/0.929738.81/0.9773
    AWSRN1397320.550638.11/0.960833.78/0.918932.26/0.900632.49/0.931638.87/0.9776
    DRCN177417974.037.63/0.958833.04/0.911831.85/0.894230.75/0.913337.55/0.9732
    CARN1592222.835237.76/0.959033.52/0.916632.09/0.897831.92/0.925638.36/0.9765
    OISR-RK249711145.771538.12/0.960933.80/0.919332.26/0.900732.48/0.931738.79/0.9773
    OISR-LF49711145.772238.12/0.960933.78/0.919632.26/0.900732.52/0.932038.80/0.9774
    MSRN60781356.881038.08/0.960733.70/0.918632.23/0.900232.29/0.930338.69/0.9772
    SeaNet74713709.1192038.08/0.960933.75/0.919032.27/0.900832.50/0.931838.76/0.9774
    TSAN39891013.1118338.22/0.961933.84/0.921832.32/0.901532.77/0.9345—/—
    PRFFN2988656.479238.18/0.961133.90/0.920732.30/0.901232.75/0.933739.02/0.9777
    3FSRCNN134.61433.16/0.914029.43/0.824228.53/0.791026.43/0.808030.98/0.9212
    SMSR993100.528134.40/0.927030.33/0.841229.10/0.805028.25/0.853633.68/0.9445
    ACAN11151051.734.46/0.927730.39/0.843529.11/0.806528.28/0.855033.61/09447
    AWSRN1476150.626334.52/0.928130.38/0.842629.16/0.806928.42/0.858033.85/0.9463
    DRCN177417974.033.85/0.921529.89/0.830428.81/0.795427.16/0.831132.31/0.9328
    CARN1592118.817734.29/0.925530.29/0.840729.06/0.803427.38/0.849333.50/0.9440
    OISR-RK25640578.636634.55/0.928230.46/0.844329.18/0.807528.50/0.859733.80/0.9442
    OISR-LF5640578.636734.56/0.928430.46/0.845029.20/0.807728.56/0.860633.78/0.9441
    MSRN6078621.247634.38/0.926230.34/0.839529.08/0.804128.08/0.855433.44/0.9427
    SeaNet73973233.299434.55/0.928230.42/0.844429.17/0.807128.50/0.859433.73/0.9463
    TSAN4174565.665034.64/0.928230.52/0.845429.20/0.808028.55/0.8602—/—
    PRFFN3167312.544134.67/0.928830.54/0.846029.23/0.808428.65/0.862134.03/0.9473
    4FSRCNN134.6830.71/0.865727.59/0.753526.98/0.715024.62/0.728027.90/0.8517
    SMSR100657.219232.12/0.893228.55/0.780827.55/0.735126.11/0.786830.54/0.9085
    ACAN1556616.532.24/0.895528.62/0.782427.59/0.737926.31/0.792230.53/0.9086
    AWSRN158791.118832.27/0.896028.69/0.784327.64/0.738526.29/0.793030.72/0.9109
    DRCN177417974.031.56/0.881028.15/0.762727.23/0.715025.14/0.751028.98/0.8816
    CARN159290.912132.13/0.893728.60/0.780627.58/0.734926.07/0.783730.47/0.9084
    OISR-RK25500412.224132.32/0.896528.72/0.784327.66/0.739026.37/0.795330.75/0.9082
    OISR-LF5500412.223932.33/0.896828.73/0.784527.66/0.738926.38/0.795330.76/0.9080
    MSRN6078365.135232.07/0.890328.60/0.775127.52/0.727326.04/0.789630.17/0.9034
    SeaNet73973065.670432.33/0.898128.72/0.785527.65/0.738826.32/0.794230.74/0.9129
    TSAN4137415.145232.40/0.897528.73/0.7847 27.67/0.739826.39/0.7955—/—
    PRFFN3131200.131632.43/0.898328.75/0.785727.70/0.739826.55/0.797430.93/0.9133
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-06-27
  • 录用日期:  2023-10-15
  • 网络出版日期:  2023-11-09
  • 刊出日期:  2024-01-29

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