典型文献
Far-field super-resolution ghost imaging with a deep neural network constraint
文献摘要：
Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing.However,GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image,imposing a practical limit for its applications.Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network.The resulting hybrid neural network does not need to pre-train on any dataset,and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit.Furthermore,the physical model imposes a constraint to the network output,making it effectively interpretable.We experimentally demonstrate the proposed GI technique by imaging a flying drone,and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio.We believe that this study provides a new framework for GI,and paves a way for its practical applications.
文献关键词：
中图分类号：
作者姓名：
Fei Wang;Chenglong Wang;Mingliang Chen;Wenlin Gong;Yu Zhang;Shensheng Han;Guohai Situ
作者机构：
Shanghai Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Shanghai 201800,China;Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Hangzhou 310024,China;CAS Center for Excellence in Ultra-intense Laser Science,Shanghai 201800,China
文献出处：
引用格式：
[1]Fei Wang;Chenglong Wang;Mingliang Chen;Wenlin Gong;Yu Zhang;Shensheng Han;Guohai Situ-.Far-field super-resolution ghost imaging with a deep neural network constraint)[J].光：科学与应用（英文版）,2022(01):27-37
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AB值：
0.60872
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