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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.

About

This repository is a PyTorch version of the paper "Multi-scale Residual Network for Image Super-Resolution" (ECCV 2018).

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