Spatiotemporal information fusion for photon-level dynamic imaging

, the system achieves 256 × 256 imaging at 200 frames per second with fewer than 2 photons per pixel. It is robust to noise, occlusion, and non-rigid deformation, and an adaptive separation module enables dual-target reconstruction under scattering interference and overlapping motion. Experiments across diverse dynamic conditions validate the approach, showing improvements of 43.12 dB in PSNR and 0.8756 in SSIM compared with conventional block compressive sensing imaging, with further improvements of 15.27 dB and 0.0118 over U-Net under photon-starved conditions. The method offers a scalable solution for high-speed, photon-limited imaging in remote sensing, transient-event observation, and traffic monitoring.

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Publication Details

Journal
Scientific Reports
Published
2026-06-19
DOI
https://doi.org/10.1038/s41598-026-58616-6
Primary Topic
Advanced Optical Sensing Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatiotemporal information fusion for photon-level dynamic imaging

Shuangping Han, Chengbing Qin, Pengyu Zan, An Li et al.
Scientific Reports
Advanced Optical Sensing Technologies
article

Spatiotemporal information fusion for photon-level dynamic imaging

Shuangping Han, Chengbing Qin, Pengyu Zan, An Li, Yaoxing Bian, Kai Song, Hongrui Liu, Liantuan Xiao, Changzhi Yu
article en

Abstract

, the system achieves 256 × 256 imaging at 200 frames per second with fewer than 2 photons per pixel. It is robust to noise, occlusion, and non-rigid deformation, and an adaptive separation module enables dual-target reconstruction under scattering interference and overlapping motion. Experiments across diverse dynamic conditions validate the approach, showing improvements of 43.12 dB in PSNR and 0.8756 in SSIM compared with conventional block compressive sensing imaging, with further improvements of 15.27 dB and 0.0118 over U-Net under photon-starved conditions. The method offers a scalable solution for high-speed, photon-limited imaging in remote sensing, transient-event observation, and traffic monitoring.

Scientific Reports
Shanxi University (CN), Taiyuan University of Technology (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 8%
Advanced Optical Sensing Technologies
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