用交替优化提升光学设计效率，兼顾速度与精度。
Computationally Efficient Information-Driven Optical Design with Interchanging Optimization
- 分步优化：先拟合测量数据，再固定模型更新光学参数
- 运行时间与内存降低至原方法的1/6，支持更复杂密度模型
- 适用于全息光学、无透镜成像等场景，适合实际系统设计
近期研究证明，仅通过测量中的信息含量即可评估成像系统，实现无需解码的通用光学设计。信息驱动编码器分析学习（IDEAL）通过梯度优化自动化该过程。本文研究IDEAL在多种成像系统中的表现，发现其存在高内存占用、长运行时间及目标函数失配问题，源于端到端可微性要求。为此提出IDEAL-IO：通过交替进行密度估计与光学参数优化，将两者解耦。先用当前测量拟合模型，再以固定模型估算信息，更新光学参数。该方法将运行时间与内存消耗降低达6倍，支持更表达力强的密度模型，引导优化获得更优设计。我们在衍射光学、无透镜成像和快照三维显微成像中验证了该方法，确立信息论优化为可扩展、实用的现实成像系统设计策略。
原文摘要 · Abstract (English)
Recent work has demonstrated that imaging systems can be evaluated through the information content of their measurements alone, enabling application-agnostic optical design that avoids computational decoding challenges. Information-Driven Encoder Analysis Learning (IDEAL) was proposed to automate this process through gradient-based optimization. In this work, we study IDEAL across diverse imaging systems and find that it suffers from high memory usage, long runtimes, and a potentially mismatched objective function due to end-to-end differentiability requirements. We introduce IDEAL with Interchanging Optimization (IDEAL-IO), a method that decouples density estimation from optical parameter optimization by alternating between fitting models to current measurements and updating optical parameters using fixed models for information estimation. This approach reduces runtime and memory usage by up to 6x while enabling more expressive density models that guide optimization toward superior designs. We validate our method on diffractive optics, lensless imaging, and snapshot 3D microscopy applications, establishing information-theoretic optimization as a practical, scalable strategy for real-world imaging system design.
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