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FDIWN-Pytorch

This repository is an official PyTorch implementation of our paper "Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution". (AAAI 2022)

Paper can be download from FDIWN.

All test datasets (Preprocessed HR images) can be downloaded from here.

All original test datasets (HR images) can be downloaded from here.

Prerequisites:

  1. Python 3.6
  2. PyTorch >= 0.4.0
  3. numpy
  4. skimage
  5. imageio
  6. matplotlib
  7. tqdm

Dataset

We used DIV2K dataset to train our model. Please download it from here.

Extract the file and put it into the Train/dataset.

Only DIV2K is used as the training dataset, and Flickr2K is not used as the training dataset !!!

Results

All our SR images can be downloaded from here.[百度网盘][提取码:0824]

All our Supplementary materials can be downloaded from here.[百度网盘][提取码:9168]

All pretrained model can be found in AAAI2022_FDIWN_premodel.

The following PSNR/SSIMs are evaluated on Matlab R2017a and the code can be referred to [Evaluate_PSNR_SSIM.m] (https://github.com/24wenjie-li/FDIWN/blob/main/FDIWN_TestCode/Evaluate_PSNR_SSIM.m).

Training

Don't use --ext sep argument on your first running.

You can skip the decoding part and use saved binaries with --ext sep argument in second time.

  cd Train/
  # FDIWN x2  LR: 48 * 48  HR: 96 * 96
  python main.py --model FDIWNx2 --save FDIWNx2 --scale 2 --n_feats 64  --reset --chop --save_results --patch_size 96 --ext sep
  
  # FDIWN x3  LR: 48 * 48  HR: 144 * 144
  python main.py --model FDIWNx3 --save FDIWNx3 --scale 3 --n_feats 64  --reset --chop --save_results --patch_size 144 --ext sep
  
  # FDIWN x4  LR: 48 * 48  HR: 192 * 192
  python main.py --model FDIWNx4 --save FDIWNx4 --scale 4 --n_feats 64  --reset --chop --save_results --patch_size 192 --ext sep

Testing

Using pre-trained model for training, all test datasets must be pretreatment by ''FDIWN_TestCode/Prepare_TestData_HR_LR.m" and all pre-trained model should be put into "FDIWN_TestCode/model/".

#FDIWN x2
python main.py --data_test MyImage --scale 2 --model FDIWNx2 --n_feats 64 --pre_train /home/ggw/wenjieli/RCAN/RCAN_TestCode/model/model_best.pt --test_only --save_results --chop --save FDIWNx2 --testpath ../LR/LRBI --testset Set5

#FDIWN x3
python main.py --data_test MyImage --scale 3 --model FDIWNx3 --n_feats 64 --pre_train /home/ggw/wenjieli/RCAN/RCAN_TestCode/model/model_best.pt --test_only --save_results --chop --save FDIWNx3 --testpath ../LR/LRBI --testset Set5

#FDIWN x4
python main.py --data_test MyImage --scale 4 --model FDIWNx4 --n_feats 64 --pre_train /home/ggw/wenjieli/RCAN/RCAN_TestCode/model/model_best.pt --test_only --save_results --chop --save FDIWNx4 --testpath ../LR/LRBI --testset Set5

Performance

Our FDIWN is trained on RGB, but as in previous work, we only reported PSNR/SSIM on the Y channel.

We use the file ''...FDIWN_TestCode/Evaluate_PSNR_SSIM'' for test.

Model Scale Params Multi-adds Set5 Set14 B100 Urban100 Manga109
FDIWN-M x2 433K 73.6G 38.03/0.9606 33.60/0.9179 32.17/0.8995 32.19/0.9284 null/null
FDIWN x2 629K 112.0G 38.07/0.9608 33.75/0.9201 32.23/0.9003 32.40/0.9305 38.85/0.9774
FDIWN-M x3 446K 35.9G 34.46/0.9274 30.35/0.8423 29.10/0.8051 28.16/0.8528 null/null
FDIWN x3 645K 51.5G 34.52/0.9281 30.42/0.8438 29.14/0.8065 28.36/0.8567 33.77/0.9456
FDIWN-M x4 454K 19.6G 32.17/0.8941 28.55/0.7806 27.58/0.7364 26.02/0.7844 null/null
FDIWN x4 664K 28.4G 32.23/0.8955 28.66/0.7829 27.62/0.7380 26.28/0.7919 30.63/0.9098

Model complexity

FDIWN gains a better trade-off between model size, performance, inference speed, and multi-adds.

Citation

If you find FDIWN useful in your research, please consider citing:

@article{gao2021feature,
  title={Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution},
  author={Gao, Guangwei and Li, Wenjie and Li, Juncheng and Wu, Fei and Lu, Huimin and Yu, Yi},
  journal={arXiv preprint arXiv:2112.08655},
  year={2021}
}

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