Monotonicity Prior for Cloud Tomography
Tamar Loeub, Aviad Levis, Vadim Holodovsky, Yoav Y. Schechner
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."
BibTeX
@inproceedings{eccv2020_monotonicityprio,
title = {Monotonicity Prior for Cloud Tomography},
author = {Tamar Loeub and Aviad Levis and Vadim Holodovsky and Yoav Y. Schechner},
booktitle = {ECCV 2020},
year = {2020}
}