用知识蒸馏让单张图像实现高质量相位恢复
Single Snapshot Distillation for Phase Coded Mask Design in Phase Retrieval
- 教师网络用多张图像训练，学生网络只用一张图
- 蒸馏后重建误差降低，性能接近多图系统
- 适合硬件受限的高效相位成像场景
相位恢复（PR）从强度测量中重建相位信息，其质量依赖于编码相位掩膜生成的多个快照。高质量恢复需多张快照，但不利于高效系统。端到端框架可联合优化光学系统与神经网络，但受物理实现限制，且易出现梯度消失。本文提出知识蒸馏（KD）方法：由多快照训练的大型教师网络，将知识传递给仅需单快照的学生网络。损失函数比较编码相位掩膜（CPM）和恢复网络特征空间。仿真显示，该方法在无教师指导时显著提升重建性能。
原文摘要 · Abstract (English)
Phase retrieval (PR) reconstructs phase information from magnitude measurements, known as coded diffraction patterns (CDPs), whose quality depends on the number of snapshots captured using coded phase masks. High-quality phase estimation requires multiple snapshots, which is not desired for efficient PR systems. End-to-end frameworks enable joint optimization of the optical system and the recovery neural network. However, their application is constrained by physical implementation limitations. Additionally, the framework is prone to gradient vanishing issues related to its global optimization process. This paper introduces a Knowledge Distillation (KD) optimization approach to address these limitations. KD transfers knowledge from a larger, lower-constrained network (teacher) to a smaller, more efficient, and implementable network (student). In this method, the teacher, a PR system trained with multiple snapshots, distills its knowledge into a single-snapshot PR system, the student. The loss functions compare the CPMs and the feature space of the recovery network. Simulations demonstrate that this approach improves reconstruction performance compared to a PR system trained without the teacher's guidance.
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