  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.
