Challenges using data-driven methods and deep learning in optical engineering

被引：3

作者：

Buquet, Julie ^{[1
,2
]}

Parent, Jocelyn ^{[2
]}

Lalonde, Jean-Francois ^{[1
]}

Thibault, Simon ^{[1
]}

机构：

[1] Univ Laval, 2325 Rue Univ, Quebec City, PQ, Canada

[2] Immervision, 2020 Blvd Robert Bourassa, Montreal, PQ, Canada

来源：

CURRENT DEVELOPMENTS IN LENS DESIGN AND OPTICAL ENGINEERING XXIII | 2022年 / 12217卷

关键词：

Computational optics; End-to-End design; Wide-angle systems; Learning-based PSF estimation; Distortion; Data-driven optical engineering;

D O I：

10.1117/12.2636262

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Data driven approaches have proven very efficient in many vision tasks and are now used for optical parameters optimization in application-specific camera design. A neural network is trained to estimate images or image quality indicators from the optical characteristics. The complexity and entanglement of such optical parameters raise new challenges we investigate in the case of wide-angle systems. We highlight them by establishing a data-driven prediction model of the RMS spot size from the distortion using mathematical or AI-based methods.

引用

页数：4

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