LKFN

Large Kernel Frequency-enhanced Network for Efficient Single Image Super-Resolution

Jiadi Chen, Chunjiang Duanmu and Huanhuan Long

Environment

• PyTorch >= 1.7 (Recommend >= 1.11)
• BasicSR = 1.4.2

Installation

pip install -r requirements.txt
python setup.py develop

How To Test

• Refer to ./options/test/LKFN for 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.pth as an example):

python basicsr/test.py -opt options/test/LKFN/test_LKFN_x4.yml

The testing results will be saved in the ./results folder.

How To Train

• Refer to ./options/train for 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.

Contact

If you have any question, please email [email protected].