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arXiv AI By Vittorio Palladino, Ahmet Enis Cetin

DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings

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DynGhost is a transformer-based architecture designed for dynamic ghost imaging, addressing two key limitations of prior methods: the lack of temporal coherence exploitation and the assumption of Gaussian noise. It alternates spatial and temporal attention blocks and incorporates a quantum-aware training framework that uses realistic detector simulations (SNSPDs, SPADs, SiPMs) and Anscombe variance-stabilizing normalization to handle Poissonian noise. Experiments on multiple benchmarks show that DynGhost surpasses traditional reconstruction methods and existing deep learning models, especially in dynamic and photon-starved scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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