-
Notifications
You must be signed in to change notification settings - Fork 1
/
README.txt
34 lines (22 loc) · 1.05 KB
/
README.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
Steps:
Process the public dataset CAVE:
1. Run ./data/png2mat.m to convert PNG files to MAT files;
2. Run ./data/data_label.m to creating training/test data and generating HR-MSI;
3. Run ./data/mat2tif.m to convert MAT files to TIF files;
Train and test the two-stream fusion network TSFN:
1. Run train.py to train the TSFN;
2. Run test.py to test the TSFN;
Run ./enhancement/enhance_adaptive.m to obtain the final HR-HSI estimation.
For any questions, feel free to email me at xiuheng.wang@mail.nwpu.edu.cn.
If you find this code helpful, please kindly cite:
@article{wang2021hyperspectral,
title={Hyperspectral image super-resolution via deep prior regularization with parameter estimation},
author={Wang, Xiuheng and Chen, Jie and Wei, Qi and Richard, C{\'e}dric},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
volume={32},
number={4},
pages={1708--1723},
year={2021},
publisher={IEEE}
}