针对光子计数噪声，用深度学习优化成像测量掩模，提升低光环境下的图像识别精度。
Deep Feature-specific Imaging
- 用深度网络学习最优测量掩模，直接在真实噪声下反向传播优化
- 在不同光子预算下分类准确率更高，尤其在泊松噪声主导时优势明显
- 对设计参数不敏感，适合低光、噪声强的计算成像场景
现代光子计数传感器日益受泊松噪声主导，而传统基于主成分分析（PCA）的特征特定成像（FSI）针对加性高斯噪声和方差保持优化，不适用于任务导向目标，导致性能不佳且优势丧失。为此，我们提出DeepFSI——一种全新的端到端光电框架。DeepFSI‘解冻’了由PCA生成的掩模，使深度神经网络能在真实泊松噪声与加性噪声条件下，直接通过梯度计算学习全局最优测量掩模。仿真与硬件实验表明，相较于基于PCA的FSI，DeepFSI在不同光子预算下均实现更高的分类准确率和更强的迁移鲁棒性，尤其在泊松噪声占主导的环境中表现突出。该方法对设计选择更具鲁棒性，在加性高斯噪声下也表现良好，为光子受限应用中的噪声鲁棒计算成像带来显著进展。
原文摘要 · Abstract (English)
Modern photon-counting sensors are increasingly dominated by Poisson noise, yet conventional feature-specific imaging (FSI), based on principal component analysis (PCA), is optimized for additive Gaussian noise and variance preservation rather than task-specific objectives, leading to suboptimal performance and a loss of its advantages under Poisson noise. To address this, we introduce DeepFSI, what we believe to be a novel end-to-end optical-electronic framework. DeepFSI "unfreezes" PCA-derived masks, enabling a deep neural network to learn globally optimal measurement masks by computing gradients directly under realistic Poisson and additive noise conditions. Simulations and hardware experiments demonstrate that DeepFSI achieves improved classification accuracy and stronger transfer robustness compared to PCAbased FSI across varying photon budgets, particularly in Poisson-noise-dominant environments. DeepFSI also exhibits enhanced robustness to design choices and performs well under additive Gaussian noise, representing a significant advance for noise-robust computational imaging in photon-limited applications.
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