Deep learning enhanced achromatic imaging with a singlet flat lens

被引:3
|
作者
Hu, Shanshan [1 ]
Xiao, Xingjian [1 ]
Ye, Xin [1 ]
Yu, Rongtao [1 ]
Chu, Yanhao [1 ]
Chen, Ji [2 ]
Zhu, Shining [1 ]
Li, Tao [1 ]
机构
[1] Nanjing Univ, Coll Engn & Appl Sci, Sch Phys, Col Engn Appl Sci,Natl Lab Solid State Microstruct, Nanjing 210093, Peoples R China
[2] Southeast Univ, Frontiers Sci Ctr Mobile Informat Commun & Secur, Sch Informat Sci & Engn, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
基金
中国国家自然科学基金;
关键词
VISIBLE WAVELENGTHS; METALENS; BANDWIDTH;
D O I
10.1364/OE.501872
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Correction of chromatic aberration is an important issue in color imaging and display. However, realizing broadband achromatic imaging by a singlet lens with high comprehensive performance still remains challenging, though many achromatic flat lenses have been reported recently. Here, we propose a deep-learning-enhanced singlet planar imaging system, implemented by a 3 mm-diameter achromatic flat lens, to achieve relatively high-quality achromatic imaging in the visible. By utilizing a multi-scale convolutional neural network (CNN) imposed to an achromatic multi-level diffractive lens (AMDL), the white light imaging qualities are significantly improved in both indoor and outdoor scenarios. Our experiments are fulfilled via a large paired imaging dataset with respect to a 3 mm-diameter AMDL, which guaranteed with achromatism in a broad wavelength range (400-1100 nm) but a relative low efficiency (-45%). After our CNN enhancement, the imaging qualities are improved by -2 dB, showing competitive achromatic and high-quality imaging with a singlet lens for practical applications.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
引用
收藏
页码:33873 / 33882
页数:10
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