usage: main.py [-h] --upscale_factor UPSCALE_FACTOR [--batchSize BATCHSIZE]
[--testBatchSize TESTBATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
[--threads THREADS] [--seed SEED]
PyTorch Super Res Example
optional arguments:
-h, --help show this help message and exit
--upscale_factor super resolution upscale factor
--batchSize training batch size
--testBatchSize testing batch size
--nEpochs number of epochs to train for
--lr Learning Rate. Default=0.0001
--threads number of threads for data loader to use Default=4
--seed random seed to use. Default=123
This example trains a spatiospectral super-resolution network on the CAVE dataset, using the first 22 HSIs for training, the remaining 10 HSIs for testing. A trained model can be downloaded at https://drive.google.com/file/d/1x60giSaTPZWZoG25sRhEbhFxVN8C_3u_/view?usp=sharing
python main.py
python main.py --mode 0 --nEpochs 100
@article{ma2022multi,
title={Multi-task Interaction learning for Spatiospectral Image Super-Resolution},
author={Qing Ma, Junjun Jiang, Xianming Liu, and Jiayi Ma},
journal={IEEE Transactions on Image Processing},
volume={},
number={},
pages={},
year={2022},
}