{
  "id": "1905.13300",
  "version": "v1",
  "published": "2019-05-23T19:11:00.000Z",
  "updated": "2019-05-23T19:11:00.000Z",
  "title": "Generative Imaging and Image Processing via Generative Encoder",
  "authors": [
    "Lin Chen",
    "Haizhao Yang"
  ],
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "This paper introduces a novel generative encoder (GE) model for generative imaging and image processing with applications in compressed sensing and imaging, image compression, denoising, inpainting, deblurring, and super-resolution. The GE model consists of a pre-training phase and a solving phase. In the pre-training phase, we separately train two deep neural networks: a generative adversarial network (GAN) with a generator $\\G$ that captures the data distribution of a given image set, and an auto-encoder (AE) network with an encoder $\\EN$ that compresses images following the estimated distribution by GAN. In the solving phase, given a noisy image $x=\\mathcal{P}(x^*)$, where $x^*$ is the target unknown image, $\\mathcal{P}$ is an operator adding an addictive, or multiplicative, or convolutional noise, or equivalently given such an image $x$ in the compressed domain, i.e., given $m=\\EN(x)$, we solve the optimization problem \\[ z^*=\\underset{z}{\\mathrm{argmin}} \\|\\EN(\\G(z))-m\\|_2^2+\\lambda\\|z\\|_2^2 \\] to recover the image $x^*$ in a generative way via $\\hat{x}:=\\G(z^*)\\approx x^*$, where $\\lambda>0$ is a hyperparameter. The GE model unifies the generative capacity of GANs and the stability of AEs in an optimization framework above instead of stacking GANs and AEs into a single network or combining their loss functions into one as in existing literature. Numerical experiments show that the proposed model outperforms several state-of-the-art algorithms.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-05-23T19:11:00.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "generative encoder",
      "image processing",
      "generative imaging",
      "ge model unifies",
      "target unknown image"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}