Name
Abstract | ||
|---|---|---|
Volume upscaling generates high-resolution volumes from low-resolution volumes to make data exploration more effective. Traditional methods, such as the simple trilinear or cubic-spline interpolation, may blur boundaries of features and lead to jagged artifacts. Inspired by recent progress in image super-resolution with Convolutional Neural Networks (CNN), we propose a CNN-based volume upscaling method. Our CNN contains three hidden layers: block extraction and representation, non-linear mapping, and reconstruction. It directly learns an end-to-end mapping from low-resolution blocks to high-resolution volume. Compared to previous methods, our CNN can preserve better structures and details of features, and provide a better volume quality in both the visualization and evaluation metrics. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3095140.3095178 | CGI |
Field | DocType | Citations |
Computer vision,Data exploration,Convolutional neural network,Computer science,Visualization,Interpolation,Artificial intelligence | Conference | 11 |
PageRank | References | Authors |
0.47 | 15 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenglei Zhou | 1 | 11 | 0.47 |
| Yule Hou | 2 | 11 | 0.47 |
| Qirui Wang | 3 | 27 | 5.12 |
| Guangxiang Chen | 4 | 11 | 0.47 |
| Jiawei Lu | 5 | 11 | 0.47 |
| Yubo Tao | 6 | 109 | 22.51 |
| Hai Lin | 7 | 142 | 29.61 |