[
  {
    "PSNR": 36.49,
    "code_links": [
      {
        "title": "yulunzhang/RDN",
        "url": "https://github.com/yulunzhang/RDN"
      }
    ],
    "date": "2018-12-25",
    "date2": 20181225,
    "model": "Residual Dense Network +",
    "paper": {
      "title": "Residual Dense Network for Image Restoration",
      "url": "https://cknow.io/lib/bd4eec871b7ad9b4"
    },
    "paper_data_uoa": "bd4eec871b7ad9b4"
  },
  {
    "PSNR": 36.31,
    "code_links": [
      {
        "title": "cszn/DnCNN",
        "url": "https://github.com/cszn/DnCNN"
      },
      {
        "title": "tum-vision/learn_prox_ops",
        "url": "https://github.com/tum-vision/learn_prox_ops"
      },
      {
        "title": "shibuiwilliam/DeepLearningDenoise",
        "url": "https://github.com/shibuiwilliam/DeepLearningDenoise"
      },
      {
        "title": "ShakedDovrat/JpegArtifactRemoval",
        "url": "https://github.com/ShakedDovrat/JpegArtifactRemoval"
      },
      {
        "title": "kfallah/NODE-Denoiser",
        "url": "https://github.com/kfallah/NODE-Denoiser"
      },
      {
        "title": "MatusPilnan/nsiete-project",
        "url": "https://github.com/MatusPilnan/nsiete-project"
      }
    ],
    "date": "2016-08-13",
    "date2": 20160813,
    "model": "DnCNN",
    "paper": {
      "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising",
      "url": "https://cknow.io/lib/8284829405abd098"
    },
    "paper_data_uoa": "8284829405abd098"
  }
]