{
  "id": "1809.08229",
  "version": "v1",
  "published": "2018-09-21T17:58:22.000Z",
  "updated": "2018-09-21T17:58:22.000Z",
  "title": "Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network",
  "authors": [
    "Rohit Pardasani",
    "Utkarsh Shreemali"
  ],
  "comment": "5 pages, 12 figures",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Image super-resolution and denoising are two important tasks in image processing that can lead to improvement in image quality. Image super-resolution is the task of mapping a low resolution image to a high resolution image whereas denoising is the task of learning a clean image from a noisy input. We propose and train a single deep learning network that we term as SuRDCNN (super-resolution and denoising convolutional neural network), to perform these two tasks simultaneously . Our model nearly replicates the architecture of existing state-of-the-art deep learning models for super-resolution and denoising. We use the proven strategy of residual learning, as supported by state-of-the-art networks in this domain. Our trained SuRDCNN is capable of super-resolving image in the presence of Gaussian noise, Poisson noise or any random combination of both of these noises.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-09-21T17:58:22.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "68T45"
    ],
    "keywords": [
      "deep convolutional network",
      "residual learning",
      "image denoising",
      "image super-resolution",
      "high resolution image"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}