Reconstructing Intensity Images from Binary Spatial Gradient Cameras

Suren Jayasuriya, Orazio Gallo, Jinwei Gu, Timo Aila, Jan Kautz

CVPRW 2017 pp. 337-343

doi:10.1109/CVPRW.2017.47 /cvprw/2017/jayasuriya2017cvprw-reconstructing/

Abstract

Binary gradient cameras extract edge and temporal information directly on the sensor, allowing for low-power, low-bandwidth, and high-dynamic-range capabilities—all critical factors for the deployment of embedded computer vision systems. However, these types of images require specialized computer vision algorithms and are not easy to interpret by a human observer. In this paper we propose to recover an intensity image from a single binary spatial gradient image with a deep auto-encoder. Extensive experimental results on both simulated and real data show the effectiveness of the proposed approach.

CVPRW Semantic Scholar

Cite

Text

Jayasuriya et al. "Reconstructing Intensity Images from Binary Spatial Gradient Cameras." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.47

Markdown

[Jayasuriya et al. "Reconstructing Intensity Images from Binary Spatial Gradient Cameras." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/jayasuriya2017cvprw-reconstructing/) doi:10.1109/CVPRW.2017.47

BibTeX

@inproceedings{jayasuriya2017cvprw-reconstructing,
  title     = {{Reconstructing Intensity Images from Binary Spatial Gradient Cameras}},
  author    = {Jayasuriya, Suren and Gallo, Orazio and Gu, Jinwei and Aila, Timo and Kautz, Jan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {337-343},
  doi       = {10.1109/CVPRW.2017.47},
  url       = {https://mlanthology.org/cvprw/2017/jayasuriya2017cvprw-reconstructing/}
}