Deep-learning-driven end-to-end metalens imaging

被引：0

作者：

Joonhyuk Seo ^{[1
]}

Jaegang Jo ^{[2
]}

Joohoon Kim ^{[3
]}

Joonho Kang ^{[4
]}

Chanik Kang ^{[1
]}

SeongWon Moon ^{[3
]}

Eunji Lee ^{[5
]}

Jehyeong Hong ^{[1
,2
,4
]}

Junsuk Rho ^{[3
,5
,6
,7
,8
]}

Haejun Chung ^{[1
,2
,4
]}

机构：

[1] Hanyang University, Department of Artificial Intelligence

[2] Hanyang University, Department of Electronic Engineering

[3] Pohang University of Science and Technology, Department of Mechanical Engineering

[4] Hanyang University, Department of Artificial Intelligence Semiconductor Engineering

[5] Pohang University of Science and Technology, Department of Chemical Engineering

[6] Pohang University of Science and Technology, Department of Electrical Engineering

[7] POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics

[8] National Institute of Nanomaterials

来源：

Advanced Photonics | 2024年 / 6卷 / 06期

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论]; TP391.41 [];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ; 080203 ;

摘要：

Recent advances in metasurface lenses(metalenses) have shown great potential for opening a new era in compact imaging, photography, light detection, and ranging(LiDAR) and virtual reality/augmented reality applications. However, the fundamental trade-off between broadband focusing efficiency and operating bandwidth limits the performance of broadband metalenses, resulting in chromatic aberration, angular aberration, and a relatively low efficiency. A deep-learning-based image restoration framework is proposed to overcome these limitations and realize end-to-end metalens imaging, thereby achieving aberration-free full-color imaging for mass-produced metalenses with 10 mm diameter. Neural-network-assisted metalens imaging achieved a high resolution comparable to that of the ground truth image.

引用

页码：71 / 83

页数：13

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