交替优化的计算高效信息驱动光学设计
Computationally Efficient Information-Driven Optical Design with Interchanging Optimization
Eric Markley, Henry Pinkard, Leyla Kabuli, Nalini Singh, Laura Waller
AI 速览：提出 IDEAL-IO，通过交替优化将密度估计与光学参数优化解耦，以更低内存和运行时间实现信息驱动的成像系统设计。
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.