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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • 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

摘要

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.