Neuro-inspired automated lens design
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917004898729984 |
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| author | Gao, Yao Sun, Lei Gao, Shaohua Jiang, Qi Yang, Kailun Hu, Weijian Qian, Xiaolong Li, Wenyong Van Gool, Luc Wang, Kaiwei |
| author_facet | Gao, Yao Sun, Lei Gao, Shaohua Jiang, Qi Yang, Kailun Hu, Weijian Qian, Xiaolong Li, Wenyong Van Gool, Luc Wang, Kaiwei |
| contents | The highly non-convex optimization landscape of modern lens design necessitates extensive human expertise, resulting in inefficiency and constrained design diversity. While automated methods are desirable, existing approaches remain limited to simple tasks or produce complex lenses with suboptimal image quality. Drawing inspiration from the synaptic pruning mechanism in mammalian neural development, this study proposes OptiNeuro--a novel automated lens design framework that first generates diverse initial structures and then progressively eliminates low-performance lenses while refining remaining candidates through gradient-based optimization. By fully automating the design of complex aspheric imaging lenses, OptiNeuro demonstrates quasi-human-level performance, identifying multiple viable candidates with minimal human intervention. This advancement not only enhances the automation level and efficiency of lens design but also facilitates the exploration of previously uncharted lens architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neuro-inspired automated lens design Gao, Yao Sun, Lei Gao, Shaohua Jiang, Qi Yang, Kailun Hu, Weijian Qian, Xiaolong Li, Wenyong Van Gool, Luc Wang, Kaiwei Optics Artificial Intelligence Machine Learning The highly non-convex optimization landscape of modern lens design necessitates extensive human expertise, resulting in inefficiency and constrained design diversity. While automated methods are desirable, existing approaches remain limited to simple tasks or produce complex lenses with suboptimal image quality. Drawing inspiration from the synaptic pruning mechanism in mammalian neural development, this study proposes OptiNeuro--a novel automated lens design framework that first generates diverse initial structures and then progressively eliminates low-performance lenses while refining remaining candidates through gradient-based optimization. By fully automating the design of complex aspheric imaging lenses, OptiNeuro demonstrates quasi-human-level performance, identifying multiple viable candidates with minimal human intervention. This advancement not only enhances the automation level and efficiency of lens design but also facilitates the exploration of previously uncharted lens architectures. |
| title | Neuro-inspired automated lens design |
| topic | Optics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.09979 |