arXiv Computer Vision
Universal Drift Correction for Multidimensional Scanning Microscopy
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arXiv Computer Vision
Sep 21
A benchmark dataset and baseline methods for four-dimensional STEM diffraction patterns
arXiv:2609.21593v1 Announce Type: new Abstract: Four-dimensional scanning transmission electron microscopy (4D-STEM) records a two-dimensional diffraction pattern at each electron-probe position, yie...
arXiv Computer Vision
Sep 23
Real-Time Atomic-Resolution Electron Phase Imaging without Probe Calibration via Ptychography-Supervised Learning
The paper introduces a ptychography‑supervised learning framework that transforms 4D‑STEM data into real‑time atomic‑resolution phase images. By training a compact model on physics‑constrained reference phase maps from a single AuPd dataset, the method predicts local phase patches directly from diffraction patterns without probe calibration or iterative optimization. The resulting workflow achieves an online latency of ~0.27 ms per probe position, a 1,000‑fold speed‑up over GPU‑accelerated ePIE, and maintains atomic‑scale lattice contrast while generalizing across materials, defocus conditions, and instruments.
arXiv Computer Vision
Sep 15
Zero-Shot Cross-Material Ptychographic Phase Reconstruction Using Deep Learning
arXiv:2609.13969v1 Announce Type: new Abstract: Ptychographic phase reconstruction is commonly formulated as an iterative inverse problem, requiring repeated object-probe updates and resulting in sub...
arXiv Computer Vision
Sep 1
Compact Snapshot Spectral Imaging with Calibration-Free Aperture Diffraction
The paper introduces ADIS, a compact, cost‑effective, calibration‑free snapshot spectral imaging system that uses only a diffractive lens, a binary mask, and a Bayer‑filtered sensor. ADIS disperses and multiplexes wavelengths, mapping energy to distinct sensor locations, and employs theoretically computed PSFs for calibration‑free spectral reconstruction. The authors further present the Orthogonal Diffraction‑Aware Unfolding Voxel Shift Transformer (ODAUVST) to solve the sparsely‑constrained inverse problem, achieving full‑resolution recovery with reduced parameters and demonstrating superior performance in real SSI experiments.
arXiv Machine Learning
Jun 10
Unsupervised Deep Learning for Limited-Angle STEM-EDX Tomography -- Application to 3D Chemical Analysis of Phase-Change Memory Devices
arXiv:2606. 10547v1 Announce Type: cross Abstract: Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited by restricted tilt ranges and low-dose conditions required to avoid beam damage.
arXiv Machine Learning
Aug 11
Multitask Scanning Probe Microscopy
arXiv:2608. 09104v1 Announce Type: cross Abstract: Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials.