{
  "id": "2102.08078",
  "version": "v1",
  "published": "2021-02-16T10:59:28.000Z",
  "updated": "2021-02-16T10:59:28.000Z",
  "title": "Restore from Restored: Single-image Inpainting",
  "authors": [
    "Eun Hye Lee",
    "Jeong Mu Kim",
    "Ji Su Kim",
    "Tae Hyun Kim"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Recent image inpainting methods show promising results due to the power of deep learning, which can explore external information available from a large training dataset. However, many state-of-the-art inpainting networks are still limited in exploiting internal information available in the given input image at test time. To mitigate this problem, we present a novel and efficient self-supervised fine-tuning algorithm that can adapt the parameters of fully pretrained inpainting networks without using ground-truth clean image in this work. We upgrade the parameters of the pretrained networks by utilizing existing self-similar patches within the given input image without changing network architectures. Qualitative and quantitative experimental results demonstrate the superiority of the proposed algorithm and we achieve state-of-the-art inpainting results on publicly available numerous benchmark datasets.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-02-16T10:59:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "single-image inpainting",
      "input image",
      "quantitative experimental results demonstrate",
      "achieve state-of-the-art inpainting results",
      "ground-truth clean image"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}