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Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3Dsample on the scale of individual cell components. Unfortunately, these 2Dimages can be too noisy to be fused into a useful 3D structure and facilitatinggood denoisers is challenging due to the lack of clean-noisy pairs.Additionally, representing a detailed 3D structure can be difficult even forclean data when using regular 3D grids. Addressing these two limitations, wesuggest a differentiable image formation model for STEM, allowing to learn ajoint model of 2D sensor noise in STEM together with an implicit 3D model. Weshow, that the combination of these models are able to successfully disentangle3D signal and noise without supervision and outperform at the same time severalbaselines on synthetic and real data.

Clean Implicit 3D Structure from Noisy 2D STEM Images