Global Search Optics: Automatically Exploring Optimal Solutions to Compact Computational Imaging Systems
arxiv(2024)
摘要
The popularity of mobile vision creates a demand for advanced compact
computational imaging systems, which call for the development of both a
lightweight optical system and an effective image reconstruction model.
Recently, joint design pipelines come to the research forefront, where the two
significant components are simultaneously optimized via data-driven learning to
realize the optimal system design. However, the effectiveness of these designs
largely depends on the initial setup of the optical system, complicated by a
non-convex solution space that impedes reaching a globally optimal solution. In
this work, we present Global Search Optics (GSO) to automatically design
compact 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. Extensive experimental results on the design of three-piece
(3P) sphere computational imaging systems illustrate that the GSO serves as a
transformative end-to-end lens design paradigm for superior global optimal
structure searching ability, which provides compact computational imaging
systems with higher imaging quality compared to traditional methods. The source
code will be made publicly available at https://github.com/wumengshenyou/GSO.
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