[
  {
    "Average PSNR": 37.91,
    "code_links": [
      {
        "title": "alexjc/neural-enhance",
        "url": "https://github.com/alexjc/neural-enhance"
      },
      {
        "title": "soumith/ganhacks",
        "url": "https://github.com/soumith/ganhacks"
      },
      {
        "title": "tetrachrome/subpixel",
        "url": "https://github.com/tetrachrome/subpixel"
      },
      {
        "title": "deepak112/Keras-SRGAN",
        "url": "https://github.com/deepak112/Keras-SRGAN"
      },
      {
        "title": "imatge-upc/3D-GAN-superresolution",
        "url": "https://github.com/imatge-upc/3D-GAN-superresolution"
      },
      {
        "title": "HighVoltageRocknRoll/sr",
        "url": "https://github.com/HighVoltageRocknRoll/sr"
      },
      {
        "title": "twhui/SRGAN-PyTorch",
        "url": "https://github.com/twhui/SRGAN-PyTorch"
      },
      {
        "title": "npielawski/pytorch_tiramisu",
        "url": "https://github.com/npielawski/pytorch_tiramisu"
      },
      {
        "title": "fengwang/subpixel_conv2d",
        "url": "https://github.com/fengwang/subpixel_conv2d"
      },
      {
        "title": "XueweiMeng/derain_filter",
        "url": "https://github.com/XueweiMeng/derain_filter"
      },
      {
        "title": "fannymonori/TF-ESPCN",
        "url": "https://github.com/fannymonori/TF-ESPCN"
      },
      {
        "title": "linxi159/Tips-and-tricks-to-train-GANs",
        "url": "https://github.com/linxi159/Tips-and-tricks-to-train-GANs"
      },
      {
        "title": "jaingaurav3/GAN-Hacks",
        "url": "https://github.com/jaingaurav3/GAN-Hacks"
      },
      {
        "title": "michael13162/DoodleGAN",
        "url": "https://github.com/michael13162/DoodleGAN"
      },
      {
        "title": "545641826/espcn",
        "url": "https://github.com/545641826/espcn"
      },
      {
        "title": "One-sixth/pixelshuffle_invert_pytorch",
        "url": "https://github.com/One-sixth/pixelshuffle_invert_pytorch"
      },
      {
        "title": "gemenerik/RTSR4k",
        "url": "https://github.com/gemenerik/RTSR4k"
      },
      {
        "title": "KeremTurgutlu/papers",
        "url": "https://github.com/KeremTurgutlu/papers"
      },
      {
        "title": "linxi159/GAN-training-tricks",
        "url": "https://github.com/linxi159/GAN-training-tricks"
      },
      {
        "title": "kingcheng2000/GAN",
        "url": "https://github.com/kingcheng2000/GAN"
      },
      {
        "title": "gs18113/ESPCN-TensorFlow2",
        "url": "https://github.com/gs18113/ESPCN-TensorFlow2"
      },
      {
        "title": "vuanhtu1993/Keras-SRGANs",
        "url": "https://github.com/vuanhtu1993/Keras-SRGANs"
      }
    ],
    "date": "2016-09-16",
    "date2": 20160916,
    "model": "ESPCN",
    "paper": {
      "title": "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network",
      "url": "https://cknow.io/lib/4b0f41a9286d4389"
    },
    "paper_data_uoa": "4b0f41a9286d4389"
  },
  {
    "Average PSNR": 37.52,
    "code_links": [
      {
        "title": "nagadomi/waifu2x",
        "url": "https://github.com/nagadomi/waifu2x"
      },
      {
        "title": "titu1994/Image-Super-Resolution",
        "url": "https://github.com/titu1994/Image-Super-Resolution"
      },
      {
        "title": "HighVoltageRocknRoll/sr",
        "url": "https://github.com/HighVoltageRocknRoll/sr"
      },
      {
        "title": "Shritesh99/100DaysofMLCodeChallenge",
        "url": "https://github.com/Shritesh99/100DaysofMLCodeChallenge"
      },
      {
        "title": "YeongHyeon/Super-Resolution_CNN",
        "url": "https://github.com/YeongHyeon/Super-Resolution_CNN"
      },
      {
        "title": "WarrenGreen/srcnn",
        "url": "https://github.com/WarrenGreen/srcnn"
      },
      {
        "title": "YeongHyeon/Super-Resolution_CNN-PyTorch",
        "url": "https://github.com/YeongHyeon/Super-Resolution_CNN-PyTorch"
      },
      {
        "title": "r06922019/butt_lion_paper_notes",
        "url": "https://github.com/r06922019/butt_lion_paper_notes"
      },
      {
        "title": "souravs17031999/60days-of-udacity",
        "url": "https://github.com/souravs17031999/60days-of-udacity"
      },
      {
        "title": "mayank-17/Project-Image-Restoration-using-SRCNN",
        "url": "https://github.com/mayank-17/Project-Image-Restoration-using-SRCNN"
      },
      {
        "title": "Pulkitdzrt/ML-Image-Super-Resolution",
        "url": "https://github.com/Pulkitdzrt/ML-Image-Super-Resolution"
      },
      {
        "title": "shreeyashyende/better_img_res_with_SRCNN",
        "url": "https://github.com/shreeyashyende/better_img_res_with_SRCNN"
      },
      {
        "title": "fourseaforfriend/waifu2x",
        "url": "https://github.com/fourseaforfriend/waifu2x"
      },
      {
        "title": "jupiterman/Super-Resolution-Images",
        "url": "https://github.com/jupiterman/Super-Resolution-Images"
      },
      {
        "title": "Shritesh99/Image_Super_Resolution",
        "url": "https://github.com/Shritesh99/Image_Super_Resolution"
      },
      {
        "title": "SWKoreaBME/paper_review",
        "url": "https://github.com/SWKoreaBME/paper_review"
      },
      {
        "title": "atheesh1998/Image-Super-Resolution",
        "url": "https://github.com/atheesh1998/Image-Super-Resolution"
      },
      {
        "title": "soham239/MLND_Capstone_Project",
        "url": "https://github.com/soham239/MLND_Capstone_Project"
      },
      {
        "title": "dalexanderch/CFD",
        "url": "https://github.com/dalexanderch/CFD"
      },
      {
        "title": "07Agarg/Image-Resolution-Enhancement-SRCNN",
        "url": "https://github.com/07Agarg/Image-Resolution-Enhancement-SRCNN"
      },
      {
        "title": "teakkkz/imageSR",
        "url": "https://github.com/teakkkz/imageSR"
      },
      {
        "title": "nikhiljangam/Image-Super-resolution-using-CNN-Slides",
        "url": "https://github.com/nikhiljangam/Image-Super-resolution-using-CNN-Slides"
      }
    ],
    "date": "2014-12-31",
    "date2": 20141231,
    "model": "SRCNN",
    "paper": {
      "title": "Image Super-Resolution Using Deep Convolutional Networks",
      "url": "https://cknow.io/lib/1baffa32efb48e9b"
    },
    "paper_data_uoa": "1baffa32efb48e9b"
  },
  {
    "Average PSNR": 36.2,
    "code_links": [
      {
        "title": "alexjc/neural-enhance",
        "url": "https://github.com/alexjc/neural-enhance"
      },
      {
        "title": "soumith/ganhacks",
        "url": "https://github.com/soumith/ganhacks"
      },
      {
        "title": "tetrachrome/subpixel",
        "url": "https://github.com/tetrachrome/subpixel"
      },
      {
        "title": "deepak112/Keras-SRGAN",
        "url": "https://github.com/deepak112/Keras-SRGAN"
      },
      {
        "title": "imatge-upc/3D-GAN-superresolution",
        "url": "https://github.com/imatge-upc/3D-GAN-superresolution"
      },
      {
        "title": "HighVoltageRocknRoll/sr",
        "url": "https://github.com/HighVoltageRocknRoll/sr"
      },
      {
        "title": "twhui/SRGAN-PyTorch",
        "url": "https://github.com/twhui/SRGAN-PyTorch"
      },
      {
        "title": "npielawski/pytorch_tiramisu",
        "url": "https://github.com/npielawski/pytorch_tiramisu"
      },
      {
        "title": "fengwang/subpixel_conv2d",
        "url": "https://github.com/fengwang/subpixel_conv2d"
      },
      {
        "title": "XueweiMeng/derain_filter",
        "url": "https://github.com/XueweiMeng/derain_filter"
      },
      {
        "title": "fannymonori/TF-ESPCN",
        "url": "https://github.com/fannymonori/TF-ESPCN"
      },
      {
        "title": "linxi159/Tips-and-tricks-to-train-GANs",
        "url": "https://github.com/linxi159/Tips-and-tricks-to-train-GANs"
      },
      {
        "title": "jaingaurav3/GAN-Hacks",
        "url": "https://github.com/jaingaurav3/GAN-Hacks"
      },
      {
        "title": "michael13162/DoodleGAN",
        "url": "https://github.com/michael13162/DoodleGAN"
      },
      {
        "title": "545641826/espcn",
        "url": "https://github.com/545641826/espcn"
      },
      {
        "title": "One-sixth/pixelshuffle_invert_pytorch",
        "url": "https://github.com/One-sixth/pixelshuffle_invert_pytorch"
      },
      {
        "title": "gemenerik/RTSR4k",
        "url": "https://github.com/gemenerik/RTSR4k"
      },
      {
        "title": "KeremTurgutlu/papers",
        "url": "https://github.com/KeremTurgutlu/papers"
      },
      {
        "title": "linxi159/GAN-training-tricks",
        "url": "https://github.com/linxi159/GAN-training-tricks"
      },
      {
        "title": "kingcheng2000/GAN",
        "url": "https://github.com/kingcheng2000/GAN"
      },
      {
        "title": "gs18113/ESPCN-TensorFlow2",
        "url": "https://github.com/gs18113/ESPCN-TensorFlow2"
      },
      {
        "title": "vuanhtu1993/Keras-SRGANs",
        "url": "https://github.com/vuanhtu1993/Keras-SRGANs"
      }
    ],
    "date": "2016-09-16",
    "date2": 20160916,
    "model": "bicubic",
    "paper": {
      "title": "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network",
      "url": "https://cknow.io/lib/4b0f41a9286d4389"
    },
    "paper_data_uoa": "4b0f41a9286d4389"
  }
]