IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation

Shuo Liu, Vijay John, Erik Blasch, Zheng Liu, Ying Huang

CVPRW 2018 pp. 1153-1160

doi:10.1109/CVPRW.2018.00160 /cvprw/2018/liu2018cvprw-ir2vi/

Abstract

Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infrared-to-visual (IR2VI) algorithm, a novel unsupervised thermal-to-visible image translation framework based on generative adversarial networks (GANs). IR2VI is able to learn the intrinsic characteristics from VI images and integrate them into IR images. Since the existing unsupervised GAN-based image translation approaches face several challenges, such as incorrect mapping and lack of fine details, we propose a structure connection module and a region-of-interest (ROI) focal loss method to address the current limitations. Experimental results show the superiority of the IR2VI algorithm over baseline methods.

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Cite

Text

Liu et al. "IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00160

Markdown

[Liu et al. "IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/liu2018cvprw-ir2vi/) doi:10.1109/CVPRW.2018.00160

BibTeX

@inproceedings{liu2018cvprw-ir2vi,
  title     = {{IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation}},
  author    = {Liu, Shuo and John, Vijay and Blasch, Erik and Liu, Zheng and Huang, Ying},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {1153-1160},
  doi       = {10.1109/CVPRW.2018.00160},
  url       = {https://mlanthology.org/cvprw/2018/liu2018cvprw-ir2vi/}
}