|M.Sc Student||Loeub Tamar|
|Subject||Hierarchical Scattering Tomography with a|
|Department||Department of Electrical and Computer Engineering||Supervisor||PROF. Yoav Schechner|
|Full Thesis text|
We enable large scale
analysis of three-dimensional heterogeneous scattering fields, through
stochastic scattering tomography which relies on radiative transfer.
This is done in a coarse-to-fine (hierarchical) approach. The approach contrasts with state-of-the-art recovery, which is based on memory-limited deterministic radiative transfer and thus applicable to smaller domains.
Tomography is achieved via stochastic gradient descent, where the gradient is derived via Monte Carlo.
We introduce a differentiable monotonicity prior, useful to express signals of monotonic tendency, such as the extinction coefficient in clouds, as a function of altitude. When deriving such a tomography approach, the case study of estimating cloud structure is important for climate research and consistent with future spaceborne imaging technologies.