Our technology is widely published. Please see the following publications from our team on applications of SenseAI and the benefits of subsampling and inpainting.
Publications
- Real-time Experimental 4-D STEM Using Compressive Sensing
- Informed Sampling Strategies for Efficient and Low-Dose Scanning (Transmission) Electron Microscopy
- Real-Time Blind Inpainting via Multi-Instance Beta-Process Factor Analysis
- Damage Diffusion Model in Scanning Transmission Electron Microscopy
- Improved STEM Imaging Using Deep Learning Based Compressed Sensing
- Recovery of Subsampled EBSD Datasets Using a 3-D Data Volume for Inpainting
- The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging
Diffusion distribution model for damage mitigation in scanning transmission electron microscopy
- Optimal Sampling and Reconstruction Strategies for Scanning Microscopes
- Sub-sampled imaging for STEM: Maximising image speed, resolution and precision through reconstruction parameter refinement
SIM-STEM Lab: Incorporating Compressed Sensing Theory for Fast STEM Simulation
- Using the Physics of Electron Beam Interactions to Determine Optimal Sampling and Image Reconstruction Strategies for High Resolution STEM
- Adaptive subsampling for cryo FIB SEM tomography
- Real time STEM image simulation using subsampling
- Overview piece on subsampling and image reconstruction
- Compressive sensing STEM with mathematics
- Simulation support for subsampling for reducing beam damage