Xiaolong Qian
Research Subjects
Research Interests
Xiaolong Qian has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Kailun Yang
Hunan University, Changsha, China
1 shared publication - Kaiwei Wang
Zhejiang University
1 shared publication - Lei Sun
State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University 310027 Hangzhou China
1 shared publication - Luc Van Gool
INSAIT, Sofia University “St. Kliment Ohridski
1 shared publication - Qi Jiang1 shared publication
- Shaohua Gao1 shared publication
Latest Publications
- Open Access
Neuro-inspired automated lens design
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.
Open AccessNeuro-inspired automated lens design
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.