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Differentiable TEM Detector: Towards Differentiable Transmission Electron Microscopy Simulation

We propose to interpret Cryogenic Electron Microscopy (CryoEM) data as a supervision for learning parameters of CryoEM microscopes. Following this formulation, we present a differentiable version of Transmission Electron Microscopy (TEM) Simulator that provides differentiability of all continuous inputs in a simulation. We demonstrate the learning capability of our simulator with two examples, detector parameter estimation and denoising. With our differentiable simulator, detector parameters can be learned from real data without time-consuming handcrafting. Besides, our simulator enables new way to denoising micrographs.

We develop this simulator with the combination of Taichi and PyTorch, exploiting kernel-based and operator-based parallel differentiable programming, which results in good speed, low memory footprint and expressive code. We call our work as Differentiable TEM Detector as there are still challenges to implement a fully differentiable transmission electron microscope simulator that can further differentiate with respect to particle positions. This work presents first steps towards a fully differentiable TEM simulator.

Finally, as a subsequence of our work, we abstract out the fuser that connects Taichi and PyTorch as an open-source library, Stannum, facilitating neural rendering and differentiable rendering in a broader context. We publish our code on GitHub.

Identiferoai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/676503
Date04 1900
CreatorsLiang, Feng
ContributorsViola, Ivan, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Heidrich, Wolfgang, Wonka, Peter, Arold, Stefan T.
Source SetsKing Abdullah University of Science and Technology
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Relationgithub:nanovis/DiffTEM, github:ifsheldon/stannum

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