Computationally Efficient Information-Driven Optical Design with Interchanging Optimization
This work addresses practical scalability challenges in optical design for imaging systems like diffractive optics and microscopy, making information-theoretic optimization more feasible for real-world applications, though it is incremental as it builds on the existing IDEAL framework.
The paper tackled the computational inefficiency and memory issues of the IDEAL method for information-driven optical design by introducing IDEAL-IO, which decouples density estimation from optimization, reducing runtime and memory usage by up to 6x and enabling more expressive models for superior designs.
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.