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.

翻译：近年来，通过数据驱动学习同时优化光学系统与下游算法的联合设计方法，已展现出优于传统分离设计方法的性能。然而，当前的联合设计方法严重依赖于人工识别初始透镜，这带来了挑战与局限，尤其对于具有多个潜在起点的复合透镜系统。在本工作中，我们提出准全局搜索光学（QGSO）方法，通过两部分自动设计基于复合透镜的计算成像系统：（i）用于自动光学设计的融合优化方法（OptiFusion），其在特定设计规范下搜索多样化的初始光学系统；（ii）高效物理感知联合优化（EPJO），其在考虑物理约束的条件下，对初始光学系统与图像重建网络进行并行联合优化，最终从所有搜索结果中选择最优解。大量实验结果表明，QGSO作为一种变革性的端到端透镜设计范式，具备卓越的全局搜索能力，能够自动提供比现有范式成像质量更高的基于复合透镜的计算成像系统。源代码将在 https://github.com/LiGpy/QGSO 公开提供。