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Spatiotemporal information fusion for photon-level dynamic imaging.

Changzhi Yu^1,2, Shuangping Han^3,4, Chengbing Qin^2,5

¹College of Physics and Optoelectronics Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.

Scientific Reports

|June 19, 2026

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This summary is machine-generated.

PhotonST-Net enhances dynamic imaging in low-light conditions by fusing spatial and temporal data. This deep reconstruction network achieves high-frame-rate, high-fidelity imaging even with minimal photons, improving remote sensing and monitoring applications.

Area of Science:

Photon-limited imaging
Computational imaging
Deep learning for image reconstruction

Background:

Dynamic reconstructions in photon-starved regimes suffer from low contrast, lost details, and flicker.
These limitations hinder high-frame-rate, high-fidelity imaging crucial for various applications.

Purpose of the Study:

To develop a novel spatiotemporal deep reconstruction network, PhotonST-Net.
To address challenges in ultra-low photon count dynamic imaging.
To enable high-speed, high-fidelity imaging under severe photon limitations.

Main Methods:

PhotonST-Net employs a frame-difference-aware architecture to fuse spatial structure and inter-frame dependencies.
The network operates at ultra-low photon counts (down to 2 photons/pixel) and high frame rates (200 fps).

Keywords:

Block compressive sensing imaging Photon-level imaging Spatiotemporal fusion network

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An adaptive separation module allows dual-target reconstruction under scattering and overlapping motion.

Main Results:

Achieved 256x256 imaging at 200 fps with <2 photons/pixel.
Demonstrated robustness to noise, occlusion, and non-rigid deformation.
Outperformed conventional block compressive sensing by 43.12 dB (PSNR) and U-Net by 15.27 dB (PSNR) in photon-starved conditions.

Conclusions:

PhotonST-Net provides a scalable solution for high-speed, photon-limited dynamic imaging.
The method significantly restores high-frequency details and temporal coherence.
Enables advanced applications in remote sensing, transient-event observation, and traffic monitoring.