LKDN

Chengxing Xie, Xiaoming Zhang, Linze Li, Haiteng Meng, Tianlin Zhang, Tianrui Li and Xiaole Zhao

Environment

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

Refer to ./options/test/LKDN 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/LKDN.
Then run the follwing codes (taking LKDN_x4.pth as an example):

python basicsr/test.py -opt options/test/LKDN/test_LKDN_x4.yml

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

Refer to ./inference for inference without the ground truth image.
Refer to ./basicsr/calculate_params_flops.py for calculating the parameters and flops.

python basicsr/train.py -opt options/train/LKDN/train_LKDN_x4.yml

More training commands can refer to this page.

The training logs and weights will be saved in the ./experiments folder.

Refer to ./pth for the validation and use of re-parameterization.

conv1x1_3x3.py, conv1x1.py and shortcut.py respectively verify the three re-parameterization methods.

del_params_ema.py simplifies the .pth file. (Remove the additional parameters retained when using EMA.)

print_pth.py prints the content of the .pth file.

reparm.py reparameterizes the model.

Note that the LKDN-S_del_rep_x4.pth is the model after re-parameterizing, and the LKDN-S_x4.pth is the model without re-parameterizing.

Benchmark results on SR ×4. Multi-Adds is calculated with a 1280 × 720 GT image.

Method	Params[K]	Multi-Adds[G]	Set5 PSNR/SSIM	Set14 PSNR/SSIM	BSD100 PSNR/SSIM	Urban100 PSNR/SSIM	Manga109 PNSR/SSIM
BSRN	352	19.4	32.35/0.8966	28.73/0.7847	27.65/0.7387	26.27/0.7908	30.84/0.9123
VapSR	342	19.5	32.38/0.8978	28.77/0.7852	27.68/0.7398	26.35/0.7941	30.89/0.9132
LKDN	322	18.3	32.39/0.8979	28.79/0.7859	27.69/0.7402	26.42/0.7965	30.97/0.9140

The inference results on benchmark datasets are available at Google Drive or Baidu Netdisk.

If you have any question, please email zxc0074869@gmail.com.