arXiv Machine Learning
Aug 5

Contrast-invariant deep ptychography neural networks

arXiv:2608. 02869v1 Announce Type: new Abstract: Ptychography neural networks suffer from scaling inconsistencies when generalizing out of distribution, limiting their real world viability.

By Albert Vong, Steven Henke, Oliver Hoidn, Hanna Ruth, Junjing Deng, Apurva Mehta, David Shapiro, Alexander Hexemer, Nicholas Schwarz
arXiv Machine Learning
Jun 30

Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

arXiv:2605. 01122v2 Announce Type: replace Abstract: Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic experimental conditions.

By Bowen Zheng, Katayun Kamdin, David Shapiro, Alexander Ditter, Dayne Sasaki, Emma Bernard, Roopali Kukreja, Petrus H. Zwart, Slavom\'ir Nem\v{s}\'ak, Apurva Mehta, Nicholas Schwarz, Alexander Hexemer, Tanny Chavez
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.

By H. Yue, C. -C. Chen, C. -N. Hsiao, J. Cheng, Y. Liu, X. Z. Liao, Steve F. Shu
arXiv Machine Learning
Sep 14

A unified self-supervised framework for single-frame Fresnel CDI and overlapped ptychography

The paper introduces a self‑supervised neural network that unifies single‑frame Fresnel coherent diffraction imaging (CDI) and overlapped ptychography. By using a fixed, pre‑estimated probe and optimizing with a Poisson negative log‑likelihood objective, the method reconstructs object patches from either a single diffraction frame or multiple overlapping measurements, achieving high SSIM scores and a ten‑fold improvement in photon‑dose efficiency. Demonstrations on synthetic patterns and real datasets from APS and LCLS show robust, high‑throughput reconstructions, with a 36× speedup over iterative solvers for a 10,304‑frame workload.

By Oliver Hoidn, Steven Henke, Albert Vong, Aashwin Mishra, Apurva Mehta, Matthew Seaberg
arXiv Computer Vision
Sep 28

Image Reconstruction from Phase with Untrained Neural Priors

The paper introduces a two‑stage, projection‑based framework for reconstructing images from only Fourier phase information, using an image‑specific neural prior alongside Fourier‑phase and spatial‑support constraints. In the first stage, the method alternates between enforcing constraints and updating the neural prior, while the second stage refines phase and support with guaranteed convergence. Experiments on 77 microscopy images show that the best variant achieves a pooled PSNR of 31.41 dB, a mean PSNR of 35.75 dB, and a mean SSIM of 0.9531, improving pooled PSNR by 1.51 dB and reducing pooled MSE by 29.3% compared to a constraint‑only baseline.

By Ene Meco, Ahmet Enis Cetin