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 reconstructed SR images can be downloaded from here .

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

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

@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}
}

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.

Now let's take a detailed introduction to the new codes.

cd MSRN/

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

Use --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.

  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 Prepare_TestData_HR_LR.m and all pre-trained model should be put into Test/model/ first.

#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


#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 Test/PSNR_SSIM_Results_BI_model.txt for PSRN/SSIM test.

Model	Scale	Set5	Set14	B100	Urban100	Manga109
old	x2	38.08/0.9605	33.74/0.9170	32.23/0.9013	32.22/0.9326	38.82/0.9868
MSRN	x2	38.08/0.9607	33.70/0.9186	32.23/0.9002	32.29/0.9303	38.69/0.9772
MSRN+	x2	38.15/0.9611	33.80/0.9192	32.28/0.9008	32.48/0.9318	38.93/0.9778
old	x3	34.38/0.9262	30.34/0.8395	29.08/0.8041	28.08/0.8554	33.44/0.9427
MSRN	x3	34.46/0.9278	30.41/0.8437	29.15/0.8064	28.33/0.8561	33.67/0.9456
MSRN+	x3	34.60/0.9286	30.52/0.8453	29.21/0.8075	28.51/0.8589	33.99/0.9473
old	x4	32.07/0.8903	28.60/0.7751	27.52/0.7273	26.04/0.7896	30.17/0.9034
MSRN	x4	32.26/0.8960	28.63/0.7836	27.61/0.7380	26.22/0.7911	30.57/0.9103
MSRN+	x4	32.40/0.8974	28.77/0.7860	27.69/0.7395	26.41/0.7952	30.93/0.9136

Convergence Analyses

MSRN x2 on DIV2K training dataset.

MSRN x3 on DIV2K training dataset.

MSRN x4 on DIV2K training dataset.