← Back to all news
arXiv Machine Learning By Oliver Hoidn, Steven Henke, Albert Vong, Aashwin Mishra, Apurva Mehta, Matthew Seaberg

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
More like this →
arXiv Computer Vision
Sep 3

SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging

SlowFast‑SCI introduces a dual‑speed deep‑unfolding framework for spectral compressive imaging that combines a slow, pre‑trained backbone with a fast, test‑time adaptation stage. The slow phase distills a priors‑based model into a compact fast‑unfolding network, while the fast phase embeds lightweight modules that self‑supervise at test time without retraining the backbone. This design yields significant reductions in parameters and FLOPs, improves out‑of‑distribution PSNR by up to 5.79 dB, and accelerates adaptation four‑fold, all while remaining modular enough to integrate with any existing deep‑unfolding system.

By Haijin Zeng, Xuan Lu, Jiezhang Cao, Kai Zhang, Yurong Zhang, Qiangqiang Shen, Guoqing Chao, Li Jiang, Yongyong Chen, Jingyong Su, Jie Liu
More like this →
arXiv Machine Learning
Sep 3

Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization

The paper introduces Scalable Bayesian Optimization of Composite Functions (SBOCF) for efficiently estimating physical parameters from scientific images, specifically targeting electron microscopy PACBED patterns. SBOCF leverages the composite structure of the image-matching objective, reducing modeled outputs from 24,649 to 11 by using patch-level summaries and correction terms. With only 50 simulator evaluations, SBOCF outperformed standard Bayesian optimization, achieving up to 290× lower median SSE on synthetic SrTiO3 benchmarks and producing accurate parameter estimates on experimental data without task-specific pretraining.

By Dasol Yoon, Poompol Buathong, Chia-Hao Lee, Yujia Zhang, David A. Muller, Peter I. Frazier
benchmarks
More like this →
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
More like this →