{
  "id": "1511.04587",
  "version": "v1",
  "published": "2015-11-14T17:36:45.000Z",
  "updated": "2015-11-14T17:36:45.000Z",
  "title": "Accurate Image Super-Resolution Using Very Deep Convolutional Networks",
  "authors": [
    "Jiwon Kim",
    "Jung Kwon Lee",
    "Kyoung Mu Lee"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "We present a highly accurate single-image super-resolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification \\cite{simonyan2015very}. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates ($10^4$ times higher than SRCNN \\cite{dong2015image}) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2015-11-14T17:36:45.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep convolutional network",
      "accurate image super-resolution",
      "method performs better",
      "large image regions",
      "highly accurate single-image super-resolution"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}