ArticleScientific reports2021
Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Surface Plasmon Resonance Imaging and Microscopy Modalities for Information-Rich, Label-Free Analysis of Biomolecular Interactions and Disease Biomarkers.Chemical & biomedical imaging · 2026Review
- Plasmonic imaging of living pancreatic beta-cell networks.Scientific reports · 2026Article
- Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering.Sensors (Basel, Switzerland) · 2023Article
- Sensing Mechanisms of Rough Plasmonic Surfaces for Protein Binding of Surface Plasmon Resonance Detection.Sensors (Basel, Switzerland) · 2023Article
- Deep learning in light-matter interactions.Nanophotonics (Berlin, Germany) · 2022Review
- Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy.Sensors (Basel, Switzerland) · 2022Article
- Generalized figure of merit for plasmonic dip measurement-based surface plasmon resonance sensors.Biomedical optics express · 2022Article
- Article
- Measurement precision enhancement of surface plasmon resonance based angular scanning detection using deep learning.Scientific reports · 2022Article
- Sensing Using Light: A Key Area of Sensors.Sensors (Basel, Switzerland) · 2021Article
- Analysis of Effects of Surface Roughness on Sensing Performance of Surface Plasmon Resonance Detection for Refractive Index Sensing Application.Sensors (Basel, Switzerland) · 2021Article
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3 authors.
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Abstract
A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10
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