Video-rate gigapixel ptychography via space-time neural field representations
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912695968595968 |
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| author | Wang, Ruihai Zhao, Qianhao Hong, Zhixuan Ma, Qiong Wang, Tianbo Jiang, Lingzhi Yang, Liming Jiang, Shaowei Huang, Feifei Nguyen, Thanh D. Shor, Leslie Gage, Daniel Lipton, Mary Anderton, Christopher Bhattacharjee, Arunima Brady, David Zheng, Guoan |
| author_facet | Wang, Ruihai Zhao, Qianhao Hong, Zhixuan Ma, Qiong Wang, Tianbo Jiang, Lingzhi Yang, Liming Jiang, Shaowei Huang, Feifei Nguyen, Thanh D. Shor, Leslie Gage, Daniel Lipton, Mary Anderton, Christopher Bhattacharjee, Arunima Brady, David Zheng, Guoan |
| contents | Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate ptychography that overcomes this barrier by exploiting spatiotemporal correlations through neural field representations. Our approach factorizes the space-time volume into low-rank spatial and temporal features, transforming SBP scaling from sequential measurements to efficient correlation extraction. The architecture employs dual networks for decoding real and imaginary field components, avoiding phase-wrapping discontinuities plagued in amplitude-phase representations. A gradient-domain loss on spatial derivatives ensures robust convergence. We demonstrate video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths. Validations span from monitoring sample dynamics of crystals, bacteria, stem cells, microneedle to characterizing time-varying probes in extreme ultraviolet experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we establish that gigapixel video is tractable with single-sensor measurements, making ptychography a high-throughput sensing tool for monitoring mesoscale dynamics without lenses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Video-rate gigapixel ptychography via space-time neural field representations Wang, Ruihai Zhao, Qianhao Hong, Zhixuan Ma, Qiong Wang, Tianbo Jiang, Lingzhi Yang, Liming Jiang, Shaowei Huang, Feifei Nguyen, Thanh D. Shor, Leslie Gage, Daniel Lipton, Mary Anderton, Christopher Bhattacharjee, Arunima Brady, David Zheng, Guoan Optics Image and Video Processing Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate ptychography that overcomes this barrier by exploiting spatiotemporal correlations through neural field representations. Our approach factorizes the space-time volume into low-rank spatial and temporal features, transforming SBP scaling from sequential measurements to efficient correlation extraction. The architecture employs dual networks for decoding real and imaginary field components, avoiding phase-wrapping discontinuities plagued in amplitude-phase representations. A gradient-domain loss on spatial derivatives ensures robust convergence. We demonstrate video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths. Validations span from monitoring sample dynamics of crystals, bacteria, stem cells, microneedle to characterizing time-varying probes in extreme ultraviolet experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we establish that gigapixel video is tractable with single-sensor measurements, making ptychography a high-throughput sensing tool for monitoring mesoscale dynamics without lenses. |
| title | Video-rate gigapixel ptychography via space-time neural field representations |
| topic | Optics Image and Video Processing |
| url | https://arxiv.org/abs/2511.06126 |