DynGhost: 用于量子探测器动态鬼成像的时序建模Transformer
DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings
AI总结：
本文提出DynGhost，一种基于Transformer的动态鬼成像方法，通过交替的空间和时间注意力模块解决传统方法在动态场景和低光条件下的局限性，利用量子感知训练框架提升真实硬件下的性能。
摘要：
鬼成像通过将结构化照明图案与标量强度测量相关联，从单像素桶探测器重建空间信息。尽管深度学习方法在静态场景中取得了显著成果，但存在两个关键局限：现有架构未能利用帧间的时间相干性，导致动态鬼成像问题未得到解决，且假设加性高斯噪声模型，而实际单光子硬件遵循泊松统计。我们提出了DynGhost（动态鬼成像Transformer），通过交替的空间和时间注意力块解决这两个限制。基于物理准确的探测器模拟（SNSPDs、SPADs、SiPMs）和Anscombe方差稳定化归一化，我们的量子感知训练框架解决了导致经典模型在真实硬件约束下失效的分布偏移。在多个基准测试中，DynGhost在动态和光子匮乏设置中优于传统重建方法和现有深度学习架构。
英文摘要：
Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensity measurements. While deep learning approaches have achieved promising results on static scenes, two critical limitations remain unaddressed: existing architectures fail to exploit temporal coherence across frames, leaving dynamic ghost imaging largely unsolved, and they assume additive Gaussian noise models that do not reflect the true Poissonian statistics of real single-photon hardware. We present DynGhost (Dynamic Ghost Imaging Transformer), a transformer architecture that addresses both limitations through alternating spatial and temporal attention blocks. Our quantum-aware training framework, based on physically accurate detector simulations (SNSPDs, SPADs, SiPMs) and Anscombe variance-stabilizing normalization, resolves the distribution shift that causes classical models to fail under realistic hardware constraints. Experiments across multiple benchmarks demonstrate that DynGhost outperforms both traditional reconstruction methods and existing deep learning architectures, with particular gains in dynamic and photon-starved settings.
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