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Video-rate gigapixel ptychography via space-time neural field representations.

Created on 01 Jul 2026

Authors

Ruihai Wang, Qianhao Zhao, Zhixuan Hong, Qiong Ma, Tianbo Wang, Lingzhi Jiang, Liming Yang, Shaowei Jiang, Feifei Huang, Thanh D Nguyen, Leslie Shor, Daniel Gage, Mary Lipton, Christopher Anderton, Arunima Bhattacharjee, David Brady, Guoan Zheng

Published in

Nature communications. Jun 30, 2026. Epub Jun 30, 2026.

Abstract

Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate lensless ptychography that overcomes this barrier through the co-design of optics, sensing scheme, and computation: a coded surface encodes the dynamic object, time-sequential sensing spreads its information across frames, and a space-time neural-field framework recovers the dynamics by exploiting spatiotemporal correlations. Our approach transforms SBP scaling from sequential measurements to efficient correlation extraction. We demonstrate video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths, achieving an SBP throughput of 20.8 gigapixels per second. Experimental validations span from monitoring mesoscale dynamics of snowflakes, bacteria, stem cells, microneedles, to characterizing time-varying dynamics in extreme-ultraviolet (EUV) experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we enable single-sensor gigapixel video that extends naturally to short-wavelength and electron regimes where radiation sensitivity has traditionally precluded high-resolution dynamic imaging.

PMID:
42380130
Bibliographic data and abstract were imported from PubMed on 01 Jul 2026.

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