# Resolution enhancement in scanning electron microscopy using deep learning

arXiv: https://arxiv.org/abs/1901.11094 · HTML: https://mlsanity.com/paper/1901.11094/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Pixel super-resolved virtual staining of label-free tissue using diffusion models](https://mlsanity.com/paper/2410.20073/) (2024)
- [Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data](https://mlsanity.com/paper/2103.03877/) (2021)
- [Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks](https://mlsanity.com/paper/2206.10152/) (2022)
- [Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry](https://mlsanity.com/paper/2411.13120/) (2024)
- [Pixel Super-Resolved Fluorescence Lifetime Imaging Using Deep Learning](https://mlsanity.com/paper/2512.16266/) (2025)
- [Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks](https://mlsanity.com/paper/2608.19117/) (2026)

## Abstract (arXiv)

We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-resolution technique by inferring unresolved features in low-resolution SEM images and comparing them with the accurately co-registered high-resolution SEM images of the same samples. Through spatial frequency analysis, we also report that our method generates images with frequency spectra matching higher resolution SEM images of the same fields-of-view. By using this technique, higher resolution SEM images can be taken faster, while also reducing both electron charging and damage to the samples.