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Laser & Optoelectronics Progress, Volume. 58, Issue 18, 1811014(2021)

Stereo Image Super-Resolution: A Survey

Yingqian Wang, Longguang Wang, Zhengyu Liang, Wei An, and Jungang Yang*
Author Affiliations
  • College of Electronic Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
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    Figures & Tables(10)
    Examples of commercial stereo cameras

    Fig. 1. Examples of commercial stereo cameras

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    Stereo camera and stereo images. (a) Schematic diagram of imaging model of stereo camera; (b) images recorded by stereo camera in Fig. 2 (a); (c) real scene images recorded by stereo camera taken from KITTI 2015 dataset[21]

    Fig. 2. Stereo camera and stereo images. (a) Schematic diagram of imaging model of stereo camera; (b) images recorded by stereo camera in Fig. 2 (a); (c) real scene images recorded by stereo camera taken from KITTI 2015 dataset[21]

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    Epipolar constraint relation of stereo images

    Fig. 3. Epipolar constraint relation of stereo images

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    Sample images in different stereo image datasets[33]

    Fig. 4. Sample images in different stereo image datasets[33]

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    Comparison of visual effects of different algorithms at 2× magnification

    Fig. 5. Comparison of visual effects of different algorithms at 2× magnification

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    Comparison of visual effects of different algorithms at 4× magnification

    Fig. 6. Comparison of visual effects of different algorithms at 4× magnification

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    PSNR values achieved by PASSRnet algorithm in different testing sets at 4× magnification . (a) Testing set in KITTI 2015; (b) testing set in Middlebury; (c) testing set in Flickr1024; (d) testing set in ETH3D

    Fig. 7. PSNR values achieved by PASSRnet algorithm in different testing sets at 4× magnification . (a) Testing set in KITTI 2015; (b) testing set in Middlebury; (c) testing set in Flickr1024; (d) testing set in ETH3D

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    • Table 1. Numerical results achieved by different algorithms

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      Table 1. Numerical results achieved by different algorithms

      AlgorithmMagnificationNumber of parameters /106KITTI 2012KITTI 2015MiddleburyFlickr1024
      PSNR/SSIMPSNR/SSIMPSNR/SSIMPSNR/SSIM
      Bicubic2×28.51/0.884228.61/0.897330.60/0.899024.94/0.8186
      VDSR[43]2×0.6630.30/0.908929.78/0.915032.77/0.910225.60/0.8534
      EDSR[44]2×38.630.96/0.922830.73/0.933534.95/0.949228.66/0.9087
      RDN[45]2×22.030.94/0.922730.70/0.933034.94/0.949128.64/0.9084
      RCAN[46]2×15.331.02/0.923230.77/0.933634.90/0.948628.63/0.9082
      StereoSR[22]2×1.0829.51/0.907329.33/0.916833.23/0.934825.96/0.8599
      PASSRnet[28]2×1.3730.81/0.919030.60/0.930034.23/0.942228.38/0.9038
      BSSRnet[31]2×1.8931.03/0.924130.74/0.934434.74/0.947528.53/0.9090
      iPASSR[32]2×1.3731.11/0.924030.81/0.934034.51/0.945428.60/0.9097
      SSRDE-FNet[26]2×2.1031.23/0.925430.90/0.935235.09/0.951128.85/0.9132
      Bicubic4×24.58/0.737224.38/0.734026.40/0.757221.82/0.6293
      VDSR[43]4×0.6625.60/0.772225.32/0.770327.69/0.794122.46/0.6718
      EDSR[44]4×38.926.35/0.801526.04/0.803929.23/0.839723.46/0.7285
      RDN[45]4×22.026.32/0.801426.04/0.804329.27/0.840423.47/0.7295
      RCAN[46]4×15.426.44/0.802926.22/0.806829.30/0.839723.48/0.7286
      StereoSR[22]4×1.0824.53/0.755524.21/0.751127.64/0.802221.70/0.6460
      PASSRnet[28]4×1.4226.34/0.798126.08/0.800228.72/0.823623.31/0.7195
      SRResNet+SAM[30]4×1.7326.44/0.801826.22/0.805428.83/0.829023.27/0.7233
      BSSRnet[31]4×1.9126.47/0.804926.17/0.807529.08/0.836223.40/0.7289
      iPASSR[32]4×1.4226.56/0.805326.32/0.808429.16/0.836723.44/0.7287
      SSRDE-FNet[26]4×2.2426.70/0.808226.43/0.811829.38/0.841123.59/0.7352

    • Table 2. Numerical results achieved by StereoSR algorithm trained on different training sets for 60 epochs at 4× magnification

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      Table 2. Numerical results achieved by StereoSR algorithm trained on different training sets for 60 epochs at 4× magnification

      Training setTesting setin KITTI 2015Testing setin MiddleburyTesting set in Flickr1024Testing set in ETH3D
      PSNR/SSIMPSNR/SSIMPSNR/SSIMPSNR/SSIM
      Training set in KITTI 201524.28/0.74126.27/0.74921.77/0.61729.63/0.831
      Training set in Middlebury23.64/0.74326.62/0.77321.64/0.64628.66/0.843
      Training set in Flickr102425.08/0.77927.85/0.80722.64/0.69230.55/0.860

    • Table 3. Numerical results achieved by PASSRnet algorithm trained on different training sets for 60 epochs at 4× magnification

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      Table 3. Numerical results achieved by PASSRnet algorithm trained on different training sets for 60 epochs at 4× magnification

      Training setTesting setin KITTI 2015Testing setin MiddleburyTesting set in Flickr1024Testing set in ETH3D
      PSNR/SSIMPSNR/SSIMPSNR/SSIMPSNR/SSIM
      Training set in KITTI 201523.13/0.70325.42/0.76221.31/0.60026.95/0.789
      Training set in Middlebury25.18/0.77428.08/0.85322.54/0.67631.39/0.864
      Training set in Flickr102425.62/0.79128.69/0.87323.25/0.71831.94/0.877
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    Yingqian Wang, Longguang Wang, Zhengyu Liang, Wei An, Jungang Yang. Stereo Image Super-Resolution: A Survey[J]. Laser & Optoelectronics Progress, 2021, 58(18): 1811014

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    Paper Information

    Category: Imaging Systems

    Received: Jun. 1, 2021

    Accepted: Jun. 26, 2021

    Published Online: Aug. 28, 2021

    The Author Email: Yang Jungang (yangjungang@nudt.edu.cn)

    DOI:10.3788/LOP202158.1811014

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology