| Author: | Yan, Zhangyuan |
| Title: | Scanning transmission electron microscopy image atomic position detection by using deep learning |
| Advisors: | Zhao, Jiong (AP) |
| Degree: | M.Phil. |
| Year: | 2024 |
| Subject: | Image processing -- Digital techniques Scanning transmission electron microscopy Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Applied Physics |
| Pages: | xi, 47 pages : color illustrations |
| Language: | English |
| Abstract: | Scanning Transmission Electron Microscopy (STEM) is a powerful imaging technique that offers high spatial resolution and detection sensitivity. However, traditional image processing techniques face limitations when dealing with complex and noisy STEM images. This paper explores the use of deep learning methods to analyze STEM images and proposes AtomID-net, an atomic finding model based on UNet. The model is trained and tested using real STEM data, addressing the limitations of simulated images. Furthermore, a new data labeling method is introduced for quick and accurate annotation of noisy images. The performance of AtomID-net is compared with traditional image processing techniques and other deep learning methods, demonstrating its superior performance and flexibility in detecting atomic positions. This research contributes to the advancement of deep learning in STEM image analysis and provides a reliable evaluation scheme. |
| Rights: | All rights reserved |
| Access: | open access |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/13026