{
  "id": "2206.09193",
  "version": "v1",
  "published": "2022-06-18T12:19:31.000Z",
  "updated": "2022-06-18T12:19:31.000Z",
  "title": "Multi-Modality Image Super-Resolution using Generative Adversarial Networks",
  "authors": [
    "Aref Abedjooy",
    "Mehran Ebrahimi"
  ],
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "Over the past few years deep learning-based techniques such as Generative Adversarial Networks (GANs) have significantly improved solutions to image super-resolution and image-to-image translation problems. In this paper, we propose a solution to the joint problem of image super-resolution and multi-modality image-to-image translation. The problem can be stated as the recovery of a high-resolution image in a modality, given a low-resolution observation of the same image in an alternative modality. Our paper offers two models to address this problem and will be evaluated on the recovery of high-resolution day images given low-resolution night images of the same scene. Promising qualitative and quantitative results will be presented for each model.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-06-18T12:19:31.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "generative adversarial networks",
      "multi-modality image super-resolution",
      "low-resolution night images",
      "high-resolution day images",
      "image-to-image translation problems"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}