Name
Abstract | ||
|---|---|---|
Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/VIS49827.2021.9623327 | 2021 IEEE Visualization Conference (VIS) |
Keywords | DocType | ISBN |
cinematic scientific visualization,science communication,public outreach,broad impact,data visualization,data processing,machine learning,deep learning,u net,image processing,data preparation | Conference | 978-1-6654-3335-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kalina Borkiewicz | 1 | 0 | 0.34 |
| Viraj Shah | 2 | 0 | 0.34 |
| J. P. Naiman | 3 | 0 | 0.34 |
| Chuanyue Shen | 4 | 0 | 0.34 |
| Stuart Levy | 5 | 17 | 2.23 |
| Jeff Carpenter | 6 | 0 | 0.34 |