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.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/676503 |
| Date | 04 1900 |
| Creators | Liang, Feng |
| Contributors | Viola, Ivan, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Heidrich, Wolfgang, Wonka, Peter, Arold, Stefan T. |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Relation | github:nanovis/DiffTEM, github:ifsheldon/stannum |
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