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.
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}
}