# Clean Implicit 3D Structure from Noisy 2D STEM Images Source: [Clean Implicit 3D Structure from Noisy 2D STEM Images]() Collection index: [llms.txt]() Data updated: 2026-01-01T14:16:52.812Z Authors: Hannah Kniesel, Timo Ropinski, Tim Bergner, Kavitha Shaga Devan, Clarissa Read, Paul Walther, Tobias Ritschel, Pedro Hermosilla Published: 2022-03-29T11:00:28.000Z ## 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 a 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Reconstruction on Synthetic Cell Structures (test)](): 3D Reconstruction; Synthetic Cell Structures (test); 2D PSNR: 20.07. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()