📝 SummarXiv

Abstract

Modern photon-counting sensors are increasingly dominated by Poisson noise, yet conventional Feature-Specific Imaging (FSI) is optimized for additive Gaussian noise, leading to suboptimal performance and a loss of its advantages under Poisson noise. To address this, we introduce DeepFSI, a novel end-to-end optical-electronic framework. DeepFSI "unfreezes" traditional FSI masks, enabling a deep neural network to learn globally optimal measurement masks by computing gradients directly under realistic Poisson and additive noise conditions. Our simulations demonstrate DeepFSI's superior feature fidelity and task performance compared to conventional FSI with predefined masks, especially 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.