Achromatic Single Metalens Imaging via Deep Neural Network

消色差透镜 光学 材料科学 物理
作者
Yunxi Dong,Bowen Zheng,Hang Li,Hong Tang,Huan Zhao,Yi Huang,Sensong An,Hualiang Zhang
出处
期刊:ACS Photonics [American Chemical Society]
卷期号:11 (4): 1645-1656 被引量:15
标识
DOI:10.1021/acsphotonics.3c01870
摘要

Meta-optics are attracting intensive interest as alternatives to traditional optical systems comprising multiple lenses and diffractive elements. Among applications, single metalens imaging is highly attractive due to the potential for achieving significant size reduction and simplified design. However, single metalenses exhibit severe chromatic performance degradation arising from material dispersion and the nature of singlet optics, making them unsuitable for full-color imaging requiring achromatic performance. In this work, we propose and validate a deep learning-based approach to enhance full-color imaging quality in single metalens systems. Our developed deep learning networks computationally reconstruct raw imaging captures by effectively refocusing the red, green, and blue primary channels, eliminating chromatic aberration and vignetting, and enhancing resolution. Importantly, these improvements are achieved without requiring any hardware modifications to the metalens itself. Through comprehensive evaluations on diverse synthetic and real-world data sets captured under various environmental conditions and focusing distances, our approach consistently demonstrates significant enhancements in image quality. By providing a practical and simplified implementation, our method overcomes the inherent limitations of meta-optics and enables the realization of achromatic metalenses without complex engineering. By addressing key challenges in full-color imaging for single metalenses, this research enables new practical applications in photography, videography, and micrography via the easy integration of metalenses with commercial cameras.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
漠之梦发布了新的文献求助10
刚刚
zoey发布了新的文献求助10
刚刚
852应助芬栀采纳,获得10
刚刚
1秒前
jiangmj1990发布了新的文献求助10
1秒前
2秒前
赵医生发布了新的文献求助10
2秒前
Xian发布了新的文献求助10
3秒前
干净的寒天发布了新的文献求助200
4秒前
SaiKerry发布了新的文献求助10
4秒前
大喵发布了新的文献求助10
5秒前
wanci应助一颗椰子糖耶采纳,获得10
5秒前
5秒前
忐忑的成仁完成签到,获得积分10
7秒前
乐乐应助_Y_X_L_采纳,获得10
7秒前
7秒前
星辰大海应助漂亮白枫采纳,获得10
11秒前
i3utter发布了新的文献求助10
11秒前
Rondab应助momo采纳,获得10
11秒前
FashionBoy应助大喵采纳,获得10
15秒前
他克莫司发布了新的文献求助100
16秒前
17秒前
17秒前
17秒前
18秒前
赘婿应助zoey采纳,获得10
19秒前
21秒前
烟花应助TT木木采纳,获得10
21秒前
SciGPT应助健忘的水蜜桃采纳,获得10
22秒前
小刘发布了新的文献求助10
23秒前
一直向前发布了新的文献求助10
23秒前
精明怜南发布了新的文献求助10
23秒前
24秒前
李健的粉丝团团长应助11采纳,获得10
24秒前
_Y_X_L_发布了新的文献求助10
25秒前
26秒前
26秒前
图图完成签到 ,获得积分10
26秒前
27秒前
行者完成签到,获得积分10
27秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989797
求助须知:如何正确求助?哪些是违规求助? 3531914
关于积分的说明 11255516
捐赠科研通 3270597
什么是DOI,文献DOI怎么找? 1805008
邀请新用户注册赠送积分活动 882181
科研通“疑难数据库(出版商)”最低求助积分说明 809190