MSRN_PyTorch

This repository is an official PyTorch implementation of the paper "Multi-scale Residual Network for Image Super-Resolution".

Paper can be download from MSRN

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

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

Our MSRN was trained and tested on the Y channel directly. However, more and more SR models are trained on RGB channels. For a fair comparison, we retrained MSRN based on EDSR code. We release the new codes and results on this project.

The old codes are moved into the OLD/ folder. The new codes are stored on MSRN/ folder.

Update 2019.06.12.1

The retraining model provided previously uses the DIV2K (1-895).

We corrected this error and provided retrained models (DIV2K 1-800) and results.

We also provided x8 results now!

Notice that, we only use 800 images (DIV2K 1-800) for training and use the latest weight file for the test.

Update 2019.06.12.2

All pretrained model can be downloaded from ECCV2018_MSRN_premodel or can be found "Test/model".

All reconstructed images can be downloaded from ECCV2018_MSRN_SR_images （Including MSRN and MSRN+）.

At the same time, we also noticed the defect of MSRN, the improved version of MSRN will be released soon.

---

Prerequisites:

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

For more informaiton, please refer to EDSR and RCAN.

Document

Train/ : all train files

Test/ : all test files

demo.sh : all running instructions

Dataset

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

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

Training

Using --ext sep_reset argument on your first running.

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

If you have enough memory, using --ext bin.

  cd Train/
  # MSRN x2  LR: 48 * 48  HR: 96 * 96
  python main.py --template MSRN --save MSRN_X2 --scale 2 --reset --save_results --patch_size 96 --ext sep_reset
  
  # MSRN x3  LR: 48 * 48  HR: 144 * 144
  python main.py --template MSRN --save MSRN_X3 --scale 3 --reset --save_results --patch_size 144 --ext sep_reset
  
  # MSRN x4  LR: 48 * 48  HR: 192 * 192
  python main.py --template MSRN --save MSRN_X4 --scale 4 --reset --save_results --patch_size 192 --ext sep_reset

Testing

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

#MSRN x2
python main.py --data_test MyImage --scale 2 --model MSRN --pre_train ../model/MSRN_x2.pt --test_only --save_results --chop --save "MSRN" --testpath ../LR/LRBI --testset Set5

#MSRN+ x2
python main.py --data_test MyImage --scale 2 --model MSRN --pre_train ../model/MSRN_x2.pt --test_only --save_results --chop --self_ensemble --save "MSRN_plus" --testpath ../LR/LRBI --testset Set5


#MSRN x3
python main.py --data_test MyImage --scale 3 --model MSRN --pre_train ../model/MSRN_x3.pt --test_only --save_results --chop --save "MSRN" --testpath ../LR/LRBI --testset Set5

#MSRN+ x3
python main.py --data_test MyImage --scale 3 --model MSRN --pre_train ../model/MSRN_x3.pt --test_only --save_results --chop --self_ensemble --save "MSRN_plus" --testpath ../LR/LRBI --testset Set5

/Users/juncheng/Documents/MSRN-PyTorch
#MSRN x4
python main.py --data_test MyImage --scale 4 --model MSRN --pre_train ../model/MSRN_x4.pt --test_only --save_results --chop --save "MSRN" --testpath ../LR/LRBI --testset Set5

#MSRN+ x4
python main.py --data_test MyImage --scale 4 --model MSRN --pre_train ../model/MSRN_x4.pt --test_only --save_results --chop --self_ensemble --save "MSRN_plus" --testpath ../LR/LRBI --testset Set5

We also introduce self-ensemble strategy to improve our MSRN and denote the self-ensembled version as MSRN+.

More running instructions can be found in demo.sh.

Performance

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

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

Model	Scale	Set5	Set14	B100	Urban100	Manga109
old (paper)	x2	38.08/0.9605	33.74/0.9170	32.23/0.9013	32.22/0.9326	38.82/0.9868
MSRN	x2	38.07/0.9608	33.68/0.9184	32.22/0.9002	32.32/0.9304	38.64/0.9771
MSRN+	x2	38.16/0.9611	33.82/0.9196	32.28/0.9080	32.47/0.9316	38.87/0.9777
old (paper)	x3	34.38/0.9262	30.34/0.8395	29.08/0.8041	28.08/0.8554	33.44/0.9427
MSRN	x3	34.48/0.9276	30.40/0.8436	29.13/0.8061	28.31/0.8560	33.56/0.9451
MSRN+	x3	34.59/0.9285	30.51/0.8454	29.20/0.8073	28.49/0.8588	33.91/0.9470
old (paper)	x4	32.07/0.8903	28.60/0.7751	27.52/0.7273	26.04/0.7896	30.17/0.9034
MSRN	x4	32.25/0.8958	28.63/0.7833	27.61/0.7377	26.22/0.7905	30.57/0.9103
MSRN+	x4	32.41/0.8975	28.76/0.7859	27.68/0.7394	26.39/0.7946	30.92/0.9136
old (paper)	x8	26.59/0.7254	24.88/0.5961	24.70/0.5410	22.37/0.5977	24.28/0.7517
MSRN	x8	26.95/0.7728	24.87/0.6380	24.77/0.5954	22.35/0.6124	24.40/0.7729
MSRN+	x8	27.07/0.7784	25.03/0.6422	24.83/0.5974	22.51/0.6182	24.62/0.7795

Convergence Analyses

MSRN x2 on DIV2K training datasets (1-800) and test datasets (896-900).

MSRN x3 on DIV2K training datasets (1-800) and test datasets (896-900).

MSRN x4 on DIV2K training datasets (1-800) and test datasets (896-900).

@InProceedings{Li_2018_ECCV,
author = {Li, Juncheng and Fang, Faming and Mei, Kangfu and Zhang, Guixu},
title = {Multi-scale Residual Network for Image Super-Resolution},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}

This implementation is for non-commercial research use only. If you find this code useful in your research, please cite the above paper.