Deep Learning-Enabled Pixel-Super-Resolved Quantitative Phase Microscopy from Single-Shot Aliased Intensity Measurement
Jie Zhou
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorYanbo Jin
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorLinpeng Lu
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorShun Zhou
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorHabib Ullah
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorJiasong Sun
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorQian Chen
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorCorresponding Author
Ran Ye
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
School of Computer and Electronic Information, Nanjing Normal University, Nanjing, Jiangsu Province, 210023 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Jiaji Li
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Chao Zuo
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorJie Zhou
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorYanbo Jin
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorLinpeng Lu
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorShun Zhou
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorHabib Ullah
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorJiasong Sun
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorQian Chen
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Search for more papers by this authorCorresponding Author
Ran Ye
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
School of Computer and Electronic Information, Nanjing Normal University, Nanjing, Jiangsu Province, 210023 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Jiaji Li
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Chao Zuo
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210019 China
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorAbstract
A new technique of deep learning-based pixel-super-resolved quantitative phase microscopy (DL-SRQPI) is proposed, achieving rapid wide-field high-resolution and high-throughput quantitative phase imaging (QPI) from single-shot low-resolution intensity measurement. By training a neural network with sufficiently paired low-resolution intensity and high-resolution phase data, the network is empowered with the capability to robustly reconstruct high-quality phase information from a single frame of an aliased intensity image. As a graphics processing units-accelerated computational method with minimal data requirement, DL-SRQPI is well-suited for live-cell imaging and accomplishes high-throughput long-term dynamic phase reconstruction. The effectiveness and feasibility of DL-SRQPI have been significantly demonstrated by comparing it with other traditional and learning-based phase retrieval methods. The proposed method has been successfully implemented into the quantitative phase reconstruction of biological samples under bright-field microscopes, overcoming pixel aliasing and improving the spatial-bandwidth product significantly. The generalization ability of DL-SRQPI is illustrated by phase reconstruction of Henrietta Lacks cells at various defocus distances and illumination patterns, and its high-throughput anti-aliased phase imaging performance is further experimentally validated. Given its capability of achieving pixel super-resolved QPI from single-shot intensity measurement over conventional bright-field microscope hardware, the proposed approach is expected to be widely adopted in life science and biomedical workflows.
Conflict of Interest
The authors declare no conflict of interest.
Open Research
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Supporting Information
| Filename | Description |
|---|---|
| lpor202300488-sup-0001-VideoS1.mp414.3 MB | Supplemental Video 1 |
| lpor202300488-sup-0002-VideoS2.mp434.6 MB | Supplemental Video 2 |
| lpor202300488-sup-0003-VideoS3.mp49.6 MB | Supplemental Video 3 |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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