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Deep shutter unrolling network

Liu, Peidong ; Cui, Zhaopeng ; Larsson, Viktor LU and Pollefeys, Marc (2020) 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 p.5940-5948
Abstract
We present a novel network for rolling shutter effect correction. Our network takes two consecutive rolling shutter images and estimates the corresponding global shutter image of the latest frame. The dense displacement field from a rolling shutter image to its corresponding global shutter image is estimated via a motion estimation network. The learned feature representation of a rolling shutter image is then warped, via the displacement field, to its global shutter representation by a differentiable forward warping block. An image decoder recovers the global shutter image based on the warped feature representation. Our network can be trained end-to-end and only requires the global shutter image for supervision. Since there is no public... (More)
We present a novel network for rolling shutter effect correction. Our network takes two consecutive rolling shutter images and estimates the corresponding global shutter image of the latest frame. The dense displacement field from a rolling shutter image to its corresponding global shutter image is estimated via a motion estimation network. The learned feature representation of a rolling shutter image is then warped, via the displacement field, to its global shutter representation by a differentiable forward warping block. An image decoder recovers the global shutter image based on the warped feature representation. Our network can be trained end-to-end and only requires the global shutter image for supervision. Since there is no public dataset available, we also propose two large datasets: the Carla-RS dataset and the Fastec-RS dataset. Experimental results demonstrate that our network outperforms the state-of-the-art methods. We make both our code and datasets available at https://github.com/ethliup/DeepUnrollNet. (Less)
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author
; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
pages
9 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
conference location
Virtual, Online, United States
conference dates
2020-06-14 - 2020-06-19
external identifiers
  • scopus:85094851252
DOI
10.1109/CVPR42600.2020.00598
language
English
LU publication?
no
id
cc33e152-c3cb-4d23-bc32-bb0cf0f901ba
date added to LUP
2022-09-06 11:55:08
date last changed
2022-09-21 14:38:31
@inproceedings{cc33e152-c3cb-4d23-bc32-bb0cf0f901ba,
  abstract     = {{We present a novel network for rolling shutter effect correction. Our network takes two consecutive rolling shutter images and estimates the corresponding global shutter image of the latest frame. The dense displacement field from a rolling shutter image to its corresponding global shutter image is estimated via a motion estimation network. The learned feature representation of a rolling shutter image is then warped, via the displacement field, to its global shutter representation by a differentiable forward warping block. An image decoder recovers the global shutter image based on the warped feature representation. Our network can be trained end-to-end and only requires the global shutter image for supervision. Since there is no public dataset available, we also propose two large datasets: the Carla-RS dataset and the Fastec-RS dataset. Experimental results demonstrate that our network outperforms the state-of-the-art methods. We make both our code and datasets available at https://github.com/ethliup/DeepUnrollNet.}},
  author       = {{Liu, Peidong and Cui, Zhaopeng and Larsson, Viktor and Pollefeys, Marc}},
  booktitle    = {{2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  language     = {{eng}},
  pages        = {{5940--5948}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Deep shutter unrolling network}},
  url          = {{http://dx.doi.org/10.1109/CVPR42600.2020.00598}},
  doi          = {{10.1109/CVPR42600.2020.00598}},
  year         = {{2020}},
}