Clean Implicit 3D Structure from Noisy 2D STEM Images

Hannah Kniesel

Ulm University

Timo Ropinski

Ulm University

Tim Bergner

Ulm University

Kavitha Shaga Devan

Ulm University

Clarissa Read

Ulm University

Paul Walther

Ulm University

Tobias Ritschel

University College London

Pedro Hermosilla

Ulm University

IEEE Conference on Computer Vision and Pattern Recognition 2022

Abstract

Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D sample on the scale of individual cell components. Unfortunately, these 2D images can be too noisy to be fused into a useful 3D structure and facilitating good denoisers is challenging due to the lack of clean-noisy pairs. Additionally, representing detailed 3D structure can be difficult even for clean data when using regular 3D grids. Addressing these two limitations, we suggest a differentiable image formation model for STEM, allowing to learn a joint model of 2D sensor noise in STEM together with an implicit 3D model. We show, that the combination of these models are able to successfully disentangle 3D signal and noise without supervision and outperform at the same time several baselines on synthetic and real data.

Bibtex

@inproceedings{kniesel2020clean,
	title={Clean Implicit 3D Structure from Noisy 2D STEM Images},
	author={Kniesel, Hannah and Ropinski, Timo and Bergner, Tim and Shaga Devan, Kavitha and Read, Clarissa and Walther, Paul and Ritschel, Tobias and Hermosilla, Pedro},
	bookTitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition}
	year={2022}
}