(Peer-Reviewed) Far-field super-resolution ghost imaging with a deep neural network constraint
Fei Wang ¹ ², Chenglong Wang 王成龙 ¹ ², Mingliang Chen 陈明亮 ¹ ², Wenlin Gong 龚文林 ¹ ², Yu Zhang ¹, Shensheng Han 韩申生 ¹ ² ³ ⁴, Guohai Situ 司徒国海 ¹ ² ³ ⁴
¹ Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
中国 上海 中国科学院上海光学精密机械研究所
² Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
中国 北京 中国科学院大学 材料科学与光电工程中心
³ Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
中国 杭州 中国科学院大学杭州高等研究院
⁴ CAS Center for Excellence in Ultra-intense Laser Science, Shanghai 201800, China
中国科学院 超强激光科学卓越创新中心
Abstract
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.
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