Spatiotemporal tomography based on scattered multiangular signals and its application for resolving evolving clouds using moving platforms

Autor:	Ronen, Roi, Schechner, Yoav Y., Eytan, Eshkol
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	We derive computed tomography (CT) of a time-varying volumetric translucent object, using a small number of moving cameras. We particularly focus on passive scattering tomography, which is a non-linear problem. We demonstrate the approach on dynamic clouds, as clouds have a major effect on Earth's climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. If these rates are used, the paper leads to a representation of the time-varying object, which simplifies 4D CT tomography. The task is achieved using gradient-based optimization. We demonstrate this in physics-based simulations and in an experiment that had yielded real-world data. Comment: 14 pages, 16 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.03223 Zobrazit plný text záznamu View this record from Arxiv