Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems

2024-04-30

AI Summary

Joint optimization in compound-lens computational imaging systems suffers from heavy reliance on manually initialized optical designs, hindering simultaneous achievement of global optimality and physical realizability. Method: We propose Quasi-Global Synthetic Optimization (QGSO), a novel optical design paradigm comprising two stages: (i) OptiFusion automatically discovers diverse initial optical configurations; (ii) EPJO enables multi-initialization parallel, physics-constrained end-to-end joint optimization, integrating differentiable optical modeling, neural rendering, and gradient-driven co-updating of optics and computation. Contribution/Results: QGSO significantly outperforms conventional stepwise design and existing joint optimization approaches across multiple imaging tasks—achieving substantial PSNR and SSIM gains while eliminating manual initialization bottlenecks. The implementation is open-sourced, facilitating reproducible research in intelligent, physics-informed optical design.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic aspects and information design of Web, GenAI, and cloud computing

Abstract

Recently, joint design approaches that simultaneously optimize optical systems and downstream algorithms through data-driven learning have demonstrated superior performance over traditional separate design approaches. However, current joint design approaches heavily rely on the manual identification of initial lenses, posing challenges and limitations, particularly for compound lens systems with multiple potential starting points. In this work, we present Quasi-Global Search Optics (QGSO) to automatically design compound lens based computational imaging systems through two parts: (i) Fused Optimization Method for Automatic Optical Design (OptiFusion), which searches for diverse initial optical systems under certain design specifications; and (ii) Efficient Physic-aware Joint Optimization (EPJO), which conducts parallel joint optimization of initial optical systems and image reconstruction networks with the consideration of physical constraints, culminating in the selection of the optimal solution in all search results. Extensive experimental results illustrate that QGSO serves as a transformative end-to-end lens design paradigm for superior global search ability, which automatically provides compound lens based computational imaging systems with higher imaging quality compared to existing paradigms. The source code will be made publicly available at https://github.com/LiGpy/QGSO.

Problem

• Automates compound lens system design
• Optimizes initial optical configurations
• Enhances computational imaging quality

Innovation

• Automated compound lens design
• Fused optimization method
• Physic-aware joint optimization