SSR-TVD: Spatial Super-Resolution for Time-Varying Data Analysis and Visualization

Pytorch implementation for SSR-TVD: Spatial Super-Resolution for Time-Varying Data Analysis and Visualization.

Prerequisites

• Linux
• CUDA >= 10.0
• Skimage
• Python >= 3.7
• Numpy
• Pytorch >= 1.0

Data format

The volume at each time step is saved as a .dat file with the little-endian format. The data is stored in column-major order, that is, z-axis goes first, then y-axis, finally x-axis. The low-resolution volumes are obtained by applying bicubic interpolation to high-resolution ones.

Training models

cd Code 

• training

python3 main.py --mode 'train' --dataset 'Vortex'

• inference

python3 main.py --mode 'inf'

Citation

@article{Han-TVCG21,
	Author = {J. Han and C. Wang},
	Journal = {IEEE Transactions on Visualization and Computer Graphics},
	Number = {6},
	Pages = {2445-2456},
	Title = {{SSR-TVD}: Spatial Super-Resolution for Time-Varying Data Analysis and Visualization},
	Volume = {28},
	Year = {2022}}

Acknowledgements

This research was supported in part by the U.S. National Science Foundation through grants IIS-1455886, CNS-1629914, DUE-1833129, and IIS-1955395.