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

TL;DR

This paper introduces a novel neural field-based method for gigapixel, video-rate ptychography that efficiently captures spatiotemporal correlations, enabling high-resolution, high-throughput imaging across various samples and wavelengths.

Contribution

The authors develop a space-time neural field representation for ptychography that overcomes previous limitations in SBP scaling and phase handling, achieving real-time gigapixel imaging.

Findings

Achieved centimeter-scale gigapixel imaging at video rates.

Resolved 308-nm linewidths in dynamic samples.

Validated across diverse biological and physical samples.

Abstract

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.