{
  "$type": "site.standard.document",
  "coverImage": {
    "$type": "blob",
    "ref": {
      "$link": "bafkreihajfwblo7jur6nelssqwmefxt6l3n72co64severewu4occold64"
    },
    "mimeType": "image/png",
    "size": 122809
  },
  "description": "This invention refers to a method of reflection removal based on a Generative Adversarial Network used for training of an ADAS camera of a vehicle, comprising an acquisition step, a training step and an inference step. [0002] In the acquisition step two identical ADAS cameras capture images having…",
  "path": "/patents/1400252",
  "publishedAt": "2025-12-10T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W40/12",
    "AUMOVIO AUTONOMOUS MOBILITY GERMANY GMBH [DE]"
  ],
  "textContent": "This invention refers to a method of reflection removal based on a Generative Adversarial Network used for training of an ADAS camera of a vehicle, comprising an acquisition step, a training step and an inference step. [0002] In the acquisition step two identical ADAS cameras capture images having essentially the same content. The first camera captures images without reflection and sends them to a first images dataset while the second camera capturing images with natural reflection and sends them to a second images dataset. [0003] In a first training step, a data processing hardware acquires two randomly sampled pair of images from the first image dataset and carries out two simultaneous altering and overlapping of the pair of images generating respectively a first mixed image as a sum of the first transmission image with a first synthetical reflection and a second mixed image as a sum of the second transmission image with a second synthetical reflection. [0004] In a second training step, the first image together with a third mixed image are altered using third augmentation parameters, the third mixed image proceeding from the second dataset, that is being naturally mixed with real reflection. [0005] The output of the first and of the second training steps enter the third training step which is carried out by using the Generative Adversarial Network GAN. [0006] In a third training step, based on a machine learning model, the Generator generates a first predicted transmission image corresponding to the first transmission image, a second predicted transmission image corresponding to the second transmission image, and a third predicted transmission image corresponding to the third transmission image; then the machine learning model is optimized for the generation of predicted transmission images as close as possible to the respective transmission images, compressed and sent to a GAN machine learning block. [0007] In the inference step, the ADAS camera acquires a single image containing reflection, its GAN machine learning block generates a predicted transmission image, having the reflection suppressed and makes the predicted transmission image available to an ADAS processing chain.",
  "title": "METHOD OF REFLECTION REMOVAL BASED ON A GENERATIVE ADVERSARIAL NETWORK USED FOR TRAINING OF AN ADAS CAMERA OF A VEHICLE"
}