3D reconstruction from focus for lensless imaging

被引:1
|
作者
Li, Ying [1 ,2 ,3 ,4 ,5 ]
Li, Zhengdai [1 ,2 ,3 ,4 ,5 ]
Chen, Kaiyu [1 ,2 ,3 ,4 ,5 ]
Guo, Youming [1 ,2 ,3 ,4 ]
Rao, Changhui [1 ,2 ,3 ,4 ]
机构
[1] Natl Lab Adapt Opt, Chengdu 610209, Peoples R China
[2] Chinese Acad Sci, Key Lab Adapt Opt, Chengdu 610209, Peoples R China
[3] Chinese Acad Sci, Inst Opt & Elect, Chengdu 610209, Peoples R China
[4] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[5] Univ Chinese Acad Sci, Sch Elect Elect & Commutat Engn, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
DEPTH ESTIMATION;
D O I
10.1364/AO.540257
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The lensless camera is an ultra-thin imaging system that utilizes encoding elements instead of lenses to perceive the light field and reconstruct it through computational methods. Early studies have demonstrated that lensless cameras can encode 3D scenes at various depths in caustic patterns with varying sizes, known as point spread functions (PSFs). By deconvolving measurements with these PSFs, the reconstruction exhibits distinct focusing effects: objects in the focal plane appear sharp, while objects in other planes become blurred. Building upon this feature, we propose a feedforward network based on depth from focus to generate the depth map and the all-in-focus image by reconstructing the focal stack and deriving the probability of pixel clarity. Using our optimization framework, we present superior and more stable depth estimation than previous methods in both simulated data and real measurements captured by our lensless camera. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
引用
收藏
页码:8212 / 8220
页数:9
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