{
  "id": "1903.00231",
  "version": "v1",
  "published": "2019-03-01T10:08:08.000Z",
  "updated": "2019-03-01T10:08:08.000Z",
  "title": "Single Image Deblurring and Camera Motion Estimation with Depth Map",
  "authors": [
    "Liyuan Pan",
    "Yuchao Dai",
    "Miaomiao Liu"
  ],
  "comment": "Accepted by WACV 2019",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Camera shake during exposure is a major problem in hand-held photography, as it causes image blur that destroys details in the captured images.~In the real world, such blur is mainly caused by both the camera motion and the complex scene structure.~While considerable existing approaches have been proposed based on various assumptions regarding the scene structure or the camera motion, few existing methods could handle the real 6 DoF camera motion.~In this paper, we propose to jointly estimate the 6 DoF camera motion and remove the non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with a single blurry image and its depth map (either direct depth measurements, or a learned depth map) as input.~We formulate our joint deblurring and 6 DoF camera motion estimation as an energy minimization problem which is solved in an alternative manner. Our model enables the recovery of the 6 DoF camera motion and the latent clean image, which could also achieve the goal of generating a sharp sequence from a single blurry image. Experiments on challenging real-world and synthetic datasets demonstrate that image blur from camera shake can be well addressed within our proposed framework.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-03-01T10:08:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "depth map",
      "single image deblurring",
      "single blurry image",
      "image blur",
      "camera shake"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}