Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 12
Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7729-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7729-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 12
Research article
 | 
09 Dec 2021
Research article |  | 09 Dec 2021

Inpainting radar missing data regions with deep learning

Andrew Geiss and Joseph C. Hardin

Viewed

Total article views: 3,701 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,663 946 92 3,701 152 87 68
  • HTML: 2,663
  • PDF: 946
  • XML: 92
  • Total: 3,701
  • Supplement: 152
  • BibTeX: 87
  • EndNote: 68
Views and downloads (calculated since 06 May 2021)
Cumulative views and downloads (calculated since 06 May 2021)

Viewed (geographical distribution)

Total article views: 3,701 (including HTML, PDF, and XML) Thereof 3,577 with geography defined and 124 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
Short summary
Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Read more