Deep Learning-Enabled Transformation of Scanning Superlens Microscopy Images into Scanning Electron Microscopy-like Large Depth-of-Field Images

Aug 25, 2026

Scanning electron microscopy (SEM) provides nanoscale imaging but depends on vacuum conditions and conductive coatings, limiting sample types and practical workflows. This work introduces a deep learning method that converts optical super-resolution (OSR) scanning superlens microscopy images into SEM-like, large depth-of-field images. A custom system captures OSR images down to ~80 nm without coatings or vacuum. A generative adversarial network learns the mapping from paired OSR and SEM images and transforms unseen OSR test images. Results show a mean peak signal-to-noise ratio increase of 0.74 dB, with strong structural detail for defect detection and biological analysis.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Method Overview

Image Acquisition with Custom Scanning Superlens Microscopy

GAN Training with Paired OSR and SEM Images

Transformation of Unseen OSR Test Images

Quantitative and Qualitative Evaluation

City University of Hong Kong [https://orcid.org/0000-0001-6434-6172](https://orcid.org/0000-0001-6434-6172) Shenyang Institute of Automation Chinese Academy of Sciences; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences University of Hong Kong [https://orcid.org/0000-0001-7687-3412](https://orcid.org/0000-0001-7687-3412) City University of Hong Kong City University of Hong Kong State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences City University of Hong Kong; City University of Hong Kong Shenzhen Research Institute (CityUSRI) Chinese Academy of Sciences City University of Hong Kong; City University of Hong Kong Shenzhen Research Institute (CityUSRI) City University of Hong Kong; City University of Hong Kong Shenzhen Research Institute (CityUSRI) City University of Hong Kong [https://orcid.org/0000-0001-9616-6213](https://orcid.org/0000-0001-9616-6213) July 11th, 2024 [https://doi.org/10.21203/rs.3.rs-4092539/v1](https://doi.org/10.21203/rs.3.rs-4092539/v1) 􀁱 􀅏 This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License There is Competing Interest. 1 Deep Learning-Enabled Transformation of Scanning Superlens Microscopy Images into Scanning Electron Microscopy-like Large Depth-of-Field Images Hui SUN1, \#, Hao LUO2,3, \#, Feifei WANG4,\#, Qingjiu CHEN1, Meng CHEN1, Xiaoduo WANG2,3, Haibo YU2,3, Guanglie ZHANG1,5,*, Lianqing LIU2,3,*, Jianping WANG5,6, Dapeng WU5,6,*, Wen Jung LI1,2,3,5,* Scanning electron microscopy (SEM) enables nanoscale imaging but requires vacuum environments and coating samples with conductive films. We present a deep learning approach to transform optical super-resolution (OSR) microscopy images into SEM-like images without these limitations. Our custom scanning superlens microscopy system acquires OSR images down to ~80 nm without coatings or vacuum. A generative adversarial network (GAN) model is trained on paired OSR and SEM images to learn the mapping between domains. The model is then used to transform previously unseen OSR test images. Quantitative analysis shows the reconstructed images achieve a mean peak signal-to-noise ratio 0.74 dB higher than the input OSR images. Qualitative assessment further demonstrates the model's ability to generate results with high structural detail. This technique overcomes key SEM constraints while preserving nanoscale resolution, promising wide applicability for challenges such as chip-level defect detection and biological sample analysis where coating or vacuum requirements pose obstacles. Conventional optical microscopes are limited by diffraction, rendering them unable to examine nanoscale objects with features smaller than approximately 200 nm. Overcoming the diffraction limits of current optical systems and developing efficient super-resolution methods could thus advance both fundamental research and nanoscale manufacturing. Accordingly, various super-resolution optical microscopy techniques have been developed over the past two decades to break the diffraction barrier. Broadly, beyond the diffraction-limit microscopy systems and techniques can be classified into four main categories based on their imaging principles: 1) fluorescence-based super-resolution microscopy1, 2) surface plasmon polariton microscopy2, 3) structured illumination-based superresolution microscopy3, and 4) microsphere lens-based super-resolution microscopy4. These new approaches have pushed the boundaries of optical microscopy and enhanced our ability to study and develop nanoscale structures. Stimulated emission depletion microscopy (STED), structured illumination m

FAQs

What limitation of SEM does the proposed approach address?

SEM requires vacuum environments and conductive coatings. The method aims to generate SEM-like images from OSR microscopy without these constraints.

How is the mapping between optical and SEM-like image domains learned?

A generative adversarial network (GAN) is trained on paired OSR and SEM images to learn the domain transformation.

What performance improvements are reported for the reconstructed images?

Quantitative analysis reports a mean peak signal-to-noise ratio increase of 0.74 dB compared with the input OSR images, and qualitative results show high structural detail.

Aug 25, 2026

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