Table of Contents

AI, Volume 1, Issue 1 (December 2019)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Order results
Result details
Select all
Export citation of selected articles as:
Open AccessArticle
Deep Learning for Super-Resolution in a Field Emission Scanning Electron Microscope
AI 2019, 1(1), 1-10; https://doi.org/10.3390/ai1010001 - 15 Oct 2019
Viewed by 181
Abstract
A field emission scanning electron microscope (FESEM) is a complex scanning electron microscope with ultra-high-resolution image scanning, instant printing, and output storage capabilities. FESEMs have been widely used in fields such as materials science, biology, and medical science. However, owing to the balance [...] Read more.
A field emission scanning electron microscope (FESEM) is a complex scanning electron microscope with ultra-high-resolution image scanning, instant printing, and output storage capabilities. FESEMs have been widely used in fields such as materials science, biology, and medical science. However, owing to the balance between resolution and field of view (FOV), when locating a target using an FESEM, it is difficult to view specific details in an image with a large FOV and high resolution simultaneously. This paper presents a deep neural network to realize super-resolution of an FESEM image. This technology can effectively improve the resolution of the acquired image without changing the physical structure of the FESEM, thus resolving the constraint problem between the resolution and FOV. Experimental results show that the apply of a deep neural network only requires a single image acquired by an FESEM to be the input. A higher resolution image with a large FOV and excellent noise reduction is obtained within a short period of time. To verify the effect of the model numerically, we evaluated the image quality by using the peak signal-to-noise ratio value and structural similarity index value, which can reach 26.88 dB and 0.7740, respectively. We believe that this technology will improve the quality of FESEM imaging and be of significance in various application fields. Full article
Show Figures

Figure 1

Back to TopTop