Journal of Sensors and Sensor Systems
Journal of Sensors and Sensor Systems
JSSS
Articles | Volume 9, issue 2
Articles | Volume 9, issue 2
https://doi.org/10.5194/jsss-9-301-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/jsss-9-301-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 2
Regular research article
 | 
24 Sep 2020
Regular research article |  | 24 Sep 2020

Deep neural networks for computational optical form measurements

Lara Hoffmann and Clemens Elster

Viewed

Total article views: 2,878 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,233 569 76 2,878 87 126
  • HTML: 2,233
  • PDF: 569
  • XML: 76
  • Total: 2,878
  • BibTeX: 87
  • EndNote: 126
Views and downloads (calculated since 24 Sep 2020)
Cumulative views and downloads (calculated since 24 Sep 2020)

Viewed (geographical distribution)

Total article views: 2,638 (including HTML, PDF, and XML) Thereof 2,634 with geography defined and 4 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Jan 2026
Download
Short summary
Deep learning has become a state-of-the-art method in machine learning, with a broad range of successful applications. Our goal is to explore the benefits of deep learning techniques for computational optical form measurements. The research is based on solving a nonlinear inverse problem aimed at the reconstruction of optical topographies from given processed interferograms. A U-Net network structure is chosen and tested on a simulated database. The obtained results are promising.
Read more
Share
Special issue
Sensors and Measurement Science International SMSI 2020