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.
- Python 3.6
- PyTorch >= 0.4.0
- numpy
- skimage
- imageio
- matplotlib
- tqdm
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 !!!
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).
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
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
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 |
FDIWN gains a better trade-off between model size, performance, inference speed, and multi-adds.
If you find FDIWN useful in your research, please consider citing:
@inproceedings{gao2022feature,
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},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={36},
number={1},
pages={661--669},
year={2022}
}