Monotonicity Prior for Cloud Tomography
Abstract
We introduce a differentiable monotonicity prior, useful to express signals of monotonic tendency. An important natural signal of this tendency is the optical extinction coefficient, as a function of altitude in a cloud. Cloud droplets become larger as vapor condenses on them in an updraft. Reconstruction of the volumetric structure of clouds is important for climate research. Data for such reconstruction is multi-view images taken simultaneously of each cloud. This acquisition mode is expected by upcoming future spaceborne imagers. We achieve three-dimensional volumetric reconstruction through stochastic scattering tomography, which is based on optimization of a cost function. Part of the cost is the monotonicity prior, which helps to improve the reconstruction quality. The stochastic tomography is based on Monte-Carlo radiative transfer. It is formulated and implemented in a coarse-to-fine form, making it scalable to large fields.
Cite
Text
Loeub et al. "Monotonicity Prior for Cloud Tomography." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58523-5_17Markdown
[Loeub et al. "Monotonicity Prior for Cloud Tomography." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/loeub2020eccv-monotonicity/) doi:10.1007/978-3-030-58523-5_17BibTeX
@inproceedings{loeub2020eccv-monotonicity,
title = {{Monotonicity Prior for Cloud Tomography}},
author = {Loeub, Tamar and Levis, Aviad and Holodovsky, Vadim and Schechner, Yoav Y.},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58523-5_17},
url = {https://mlanthology.org/eccv/2020/loeub2020eccv-monotonicity/}
}