Large Kernel Frequency-enhanced Network for Efficient Single Image Super-Resolution
Jiadi Chen, Chunjiang Duanmu and Huanhuan Long
- PyTorch >= 1.7 (Recommend >= 1.11)
- BasicSR = 1.4.2
pip install -r requirements.txt
python setup.py develop
- Refer to
./options/test/LKFNfor the configuration file of the model to be tested, and prepare the testing data and pretrained model. - The pretrained models are available in
./experiments/pretrained_models/LKFN. - Then run the follwing codes (taking
LKFN_x4.pthas an example):
python basicsr/test.py -opt options/test/LKFN/test_LKFN_x4.yml
The testing results will be saved in the ./results folder.
- Refer to
./options/trainfor the configuration file of the model to train. - Preparation of training data can refer to this page.
- The training command is like:
python basicsr/train.py -opt options/train/LKFN/train_LKFN_x4.yml
More training commands can refer to this page.
The training logs and weights will be saved in the ./experiments folder.
If you have any question, please email [email protected].