NTIRE2017 Super-resolution Challenge: SNU_CVLab

Introduction

This is our project repository for CVPR 2017 Workshop (2nd NTIRE).

We, Team SNU_CVLab, (Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee of Computer Vision Lab, Seoul National University) are winners of NTIRE2017 Challenge on Single Image Super-Resolution.

Our paper was published in CVPR 2017 workshop (2nd NTIRE), and won the Best Paper Award of the workshop challenge track.

Please refer to our paper for details.

If you find our work useful in your research or publication, please cite our work:

[1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution," 2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with CVPR 2017. [PDF] [arXiv] [Slide]

@InProceedings{Lim_2017_CVPR_Workshops,
  author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
  title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {July},
  year = {2017}
}

In this repository, we provide

• Our model architecture description (EDSR, MDSR)
• NTIRE2017 Super-resolution Challenge Results
• Demo & Training code
• Trained models (EDSR, MDSR)
• Datasets we used (DIV2K, Flickr2K)
• Super-resolution examples

The code is based on Facebook's Torch implementation of ResNet (facebook/fb.resnet.torch).

We also provide PyTorch version of EDSR and MDSR. (Until now, only some models are available.)

Model Architecture

EDSR (Single-scale model. We provide scale x2, x3, x4 models).

MDSR (Multi-scale model. It can handle x2, x3, x4 super-resolution in a single model).

Note that the MDSR architecture for the challenge and for the paper[1] is slightly different. During the challenge, MDSR had variation between two challenge tracks. While we had scale-specific feature extraction modules for track 2:unknown downscaling, we didn't use the scale-specific modules for track 1:bicubic downscaling.

We later unified the MDSR model in our paper[1] by including scale-specific modules for both cases. From now on, unless specified as "challenge", we describe the models described in the paper.

NTIRE2017 Super-resolution Challenge Results

We proposed 2 methods and they won the 1st (EDSR) and 2nd (MDSR) place.

We have also compared the super-resolution performance of our models with previous state-of-the-art methods.

About our code

Dependencies

• Torch7
• cuDNN
• nccl (Optional, for faster GPU communication)

Our code is tested under Ubuntu 14.04 and 16.04 environment with Titan X GPUs (12GB VRAM).

Code

Clone this repository into any place you want. You may follow the example below.

makeReposit = [/the/directory/as/you/wish]
mkdir -p $makeReposit/; cd $makeReposit/
git clone https://github.com/LimBee/NTIRE2017.git

Quick Start (Demo)

You can test our super-resolution algorithm with your own images.

We assume the images are downsampled by bicubic interpolation.

1. Download our models

   cd $makeReposit/NTIRE2017/demo/model/
   
   # Our models for the paper[1]
   wget https://cv.snu.ac.kr/research/EDSR/model_paper.tar

   Or, use the link: model_paper.tar

   (If you would like to run the models we used during the challenge, please contact us.)

   After downloading the .tar files, make sure that the model files are placed in proper locations. For example,

   $makeReposit/NTIRE2017/demo/model/bicubic_x2.t7
   $makeReposit/NTIRE2017/demo/model/bicubic_x3.t7
   ...

2. Place your low-resolution test images at

   $makeReposit/NTIRE2017/demo/img_input/

   The demo code will read .jpg, .jpeg, .png format images.

3. Run test.lua

   You can run different models and scales by changing input arguments.

   # To run for scale 2, 3, or 4, set -scale as 2, 3, or 4
   # To run EDSR+ and MDSR+, you need to set -selfEnsemble as true
   
   cd $makeReposit/NTIRE2017/demo
   
   # Test EDSR (scale 2)
   th test.lua -model EDSR_x2 -selfEnsemble false
   
   # Test EDSR+ (scale 2)
   th test.lua -model EDSR_x2 -selfEnsemble true
   
   # Test MDSR (scale 2)
   th test.lua -model MDSR -scale 2 -selfEnsemble false
   
   # Test MDSR+ (scale 2)
   th test.lua -model MDSR -scale 2 -selfEnsemble true

   (Note: To run the MDSR, model name should include multiscale or MDSR. e.g. multiscale_blahblahblah.t7)

   The result images will be located at

   $makeReposit/NTIRE2017/demo/img_output/

   • Here are some optional argument examples you can adjust. Please refer to the following explanation.

   # You can test our model with multiple GPU. (n = 1, 2, 4)
   -nGPU       [n]
   
   # You must specify this directory. Default is /var/tmp/dataset
   -dataDir    [$makeData]
   -dataset    [DIV2K | myData]
   -save       [Folder name]
   
   # Please see our paper[1] if you want to know about self-ensemble.
   -selfEnsemble   [true | false]
   
   # Please reduce the chopSize when you see 'out of memory'.
   # The optimal size of S can be vary depend on your maximum GPU memory.
   -chopSize   [S]   

4. (Optional) Evaluate PSNR and SSIM if you have ground-truth HR images

   Place the GT images at

   $makeReposit/NTIRE2017/demo/img_target

   Evaluation is done by running the MATLAB script.

   matlab -nodisplay <evaluation.m

   If you don't want to calculate SSIM, please modify evaluation.m file as below. (Calculating SSIM of large image is very slow for 3 channel images.)

   line 6:     psnrOnly = false; -> psnrOnly = true;
   

You can reproduce our final results by running makeFinal.sh in NTIRE2017/demo directory. Please uncomment the command you want to execute in the file.

sh makeFinal.sh

Dataset

If you want to train or evaluate our models with DIV2K or Flickr2K dataset, please download the dataset from here. Place the tar file to the location you want. (We recommend /var/tmp/dataset/) If the dataset is located otherwise, you have to change the optional argument -dataset for training and test.

• DIV2K from NTIRE2017

  makeData = /var/tmp/dataset/ # We recommend this path, but you can freely change it.
  mkdir -p $makeData/; cd $makedata/
  tar -xvf DIV2K.tar

  You should have the following directory structure:

  /var/tmp/dataset/DIV2K/DIV2K_train_HR/0???.png
/var/tmp/dataset/DIV2K/DIV2K_train_LR_bicubic/X?/0???.png
/var/tmp/dataset/DIV2K/DIV2K_train_LR_unknown/X?/0???.png
  

• Flickr2K dataset collected by ourselves using Flickr API

  makeData = /var/tmp/dataset/
  mkdir -p $makeData/; cd $makedata/
  wget https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
  tar -xvf Flickr2K.tar

  You should have the following directory structure:

  /var/tmp/dataset/Flickr2K/Flickr2K_HR/00????.png
/var/tmp/dataset/Flickr2K/Flickr2K_train_LR_bicubic/X?/00????x?.png
/var/tmp/dataset/Flickr2K/Flickr2K_train_LR_unknown/X?/00????x?.png
  

  We also provide the code we used for collecting the Flickr2K images at

  $makeReposit/NTIRE2017/code/tools/Flickr2K/

  Use your own flickr API keys to use the script.

  During the challenge, we additionally generated training data by learning simple downsampler networks from DIV2K dataset track 2.
  You can download the downsampler models from here.

To make data loading faster, you can convert the dataset into binary .t7 files

• Convert DIV2K dataset from .png to into .t7 files

  cd $makeReposit/NTIRE2017/code/tools
  
  # Choose one among below
  
  # This command generates multiple t7 files for
  # each image in DIV2K_train_HR folder (Requires ~2GB RAM for training)
  th png_to_t7.lua -apath $makeData -dataset DIV2K -split true
  
  # This command generates a single t7 file that contains
  # every image in DIV2K_train_HR folder (Requires ~16GB RAM for training)
  th png_to_t7.lua -apath $makeData -dataset DIV2K -split false

• Convert Flickr2K dataset into .t7 files

  cd $makeReposit/NTIRE2017/code/tools
  
  # This command generates multiple t7 files for
  # each image in Flickr2K_HR folder
  th png_to_t7.lua -apath $makeData -dataset Flickr2K -split true

You can also use .png files too. Please see below Training section for the details.

Training

1. To train our baseline model, please run the following command:

   th main.lua         # This model is not our final model!

   • Here are some optional arguments you can adjust. If you have any problem, please refer following lines. You can check out details in NTIRE2017/code/opts.lua.

     # You can train the model with multiple GPU. (Not multi-scale model.)
     -nGPU       [n]
     
     # Number of threads for data loading.
     -nThreads   [n]   
     
     # Please specify this directory. Default is /var/tmp/dataset
     -datadir    [$makeData]  
     
     # You can make an experiment folder with the name you want.
     -save       [Folder name]
     
     # You can resume your experiment from the last checkpoint.
     # Please do not set -save and -load at the same time.
     -load       [Folder name]     
     
     # png < t7 < t7pack - requires larger memory
     # png > t7 > t7pack - requires faster CPU & Storage
     -datatype   [png | t7 | t7pack]     
     
     # Please increase the splitBatch when you see 'out of memory' during training.
     # S should be the power of 2. (1, 2, 4, ...)
     -splitBatch [S]
     
     # Please reduce the chopSize when you see 'out of memory' during test.
     # The optimal size of S can be vary depend on your maximum GPU memory.
     -chopSize   [S]

2. To train our EDSR and MDSR, please use the training.sh in NTIRE2017/code directory. You have to uncomment the line you want to execute.

   cd $makeReposit/NTIRE2017/code
   sh training.sh

   Some model may require pre-trained bicubic scale 2 or bicubic multiscale model. Here, we assume that you already downloaded bicubic_x2.t7 and bicubic_multiscale.t7 in the NTIRE2017/demo/model directory. Otherwise, you can create them yourself. It is also possible to start the traning from scratch by removing -preTrained option in training.sh.

Results

NTIRE2017 SR Challenge: Unknown Down-sampling Track