DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imaging with Quantum Detectors
Researchers have developed DynGhost, a novel transformer architecture designed for dynamic ghost imaging using quantum detectors. This model addresses limitations in existing methods by incorporating temporal coherence across frames and employing a quantum-aware training framework that accounts for realistic detector noise statistics. Experiments show DynGhost surpasses traditional and current deep learning approaches, especially in dynamic and low-photon scenarios. AI
IMPACT Introduces a new transformer architecture for dynamic ghost imaging, improving performance in low-light and dynamic conditions.