Computationally Efficient Information-Driven Optical Design with Interchanging Optimization

2025-07-10

AI Summary

IDEAL suffers from high memory consumption, prolonged runtime, and objective–performance misalignment due to its reliance on end-to-end differentiability in information-driven optical design. To address these limitations, we propose IDEAL-IO, which decouples density estimation from optical parameter optimization via alternating updates—eliminating the requirement for full-chain differentiability and enabling flexible incorporation of non-differentiable density estimators (e.g., histograms, kernel density estimation). This ensures the optimization objective aligns more closely with actual imaging metrics. IDEAL-IO alternates between fixed-distribution estimation and gradient-based parameter refinement, substantially improving computational efficiency and scalability. Evaluated on diffractive optics, lensless imaging, and snapshot 3D microscopy, IDEAL-IO achieves up to 6× speedup and memory efficiency gains over IDEAL, establishing a practical, scalable paradigm for high-performance, large-scale optical system design.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

Abstract

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. 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.

Problem

• High memory usage in IDEAL optical design optimization
• Long runtimes in gradient-based information-driven imaging systems
• Mismatched objective function due to differentiability requirements

Innovation

• Interchanging optimization decouples density estimation
• Reduces runtime and memory usage significantly
• Enables expressive density models for superior designs