ML Anthology

MU-Net: Deep Learning-Based Thermal IR Image Estimation from RGB Image

Yumi Iwashita, Kazuto Nakashima, Sir Rafol, Adrian Stoica, Ryo Kurazume
CVPRW 2019 pp. 1022-1028
doi:10.1109/CVPRW.2019.00134 /cvprw/2019/iwashita2019cvprw-munet/

Abstract

Terrain imagery collected by satellite remote sensing or by rover on-board sensors is the primary source for terrain classification used in determining terrain traversibility and mission plans for planetary rovers. Mapping models between RGB and IR for terrain classes are learned from real RGB and IR data examples in the same or similar terrain. This paper adds a new class of deep learning architectures called MU-Net (Multiple U-Net) and shows its efficiency in deriving better RGB-to-IR mapping models, improving over past work the estimation of thermal IR images from incoming RGB images and learned RGB-IR mappings.

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Cite

Text

Iwashita et al. "MU-Net: Deep Learning-Based Thermal IR Image Estimation from RGB Image." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00134

Markdown

[Iwashita et al. "MU-Net: Deep Learning-Based Thermal IR Image Estimation from RGB Image." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/iwashita2019cvprw-munet/) doi:10.1109/CVPRW.2019.00134

BibTeX

@inproceedings{iwashita2019cvprw-munet,
  title     = {{MU-Net: Deep Learning-Based Thermal IR Image Estimation from RGB Image}},
  author    = {Iwashita, Yumi and Nakashima, Kazuto and Rafol, Sir and Stoica, Adrian and Kurazume, Ryo},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {1022-1028},
  doi       = {10.1109/CVPRW.2019.00134},
  url       = {https://mlanthology.org/cvprw/2019/iwashita2019cvprw-munet/}
}