FlowTIE: Flow-based Transport of Intensity Equation for Phase Gradient Estimation from 4D-STEM Data
This addresses phase reconstruction challenges in materials science, particularly for thick specimens, but appears incremental as it builds on existing TIE and neural network methods.
The paper tackled phase reconstruction from 4D-STEM data by introducing FlowTIE, a neural network framework that integrates the Transport of Intensity Equation with a flow-based representation, resulting in improved accuracy and robustness under dynamical scattering conditions for thick specimens.
We introduce FlowTIE, a neural-network-based framework for phase reconstruction from 4D-Scanning Transmission Electron Microscopy (STEM) data, which integrates the Transport of Intensity Equation (TIE) with a flow-based representation of the phase gradient. This formulation allows the model to bridge data-driven learning with physics-based priors, improving robustness under dynamical scattering conditions for thick specimen. The validation on simulated datasets of crystalline materials, benchmarking to classical TIE and gradient-based optimization methods are presented. The results demonstrate that FlowTIE improves phase reconstruction accuracy, fast, and can be integrated with a thick specimen model, namely multislice method.