One-click script

Make sure you are in the root folder. You should also havepython>=3.6, tensorflow(-gpu)>=1.12.0 and pytorch>=1.0.0 installed manually.

Before running one_click_ntire19_rsr.sh for Real Image Super Resolution, set two paths: RSR_TEST_DIR for testing images and RSR_SAVE_DIR for saving results.

It's recommended to use absolute path.

RSR_TEST_DIR=/bla/bla/bla
RSR_SAVE_DIR=/bli/bli/bli
. one_click_ntire19_rsr.sh

Before running one_click_ntire19_drn.sh for sRGB Image Denoising, set two paths: DRN_TEST_MAT for testing mat file and DRN_SAVE_DIR for saving results.

It's recommended to use absolute path.

DRN_TEST_MAT=/bla/bla/bla/BenchmarkNoisyBlocksSrgb.mat
DRN_SAVE_DIR=/bli/bli/bli
. one_click_ntire19_drn.sh

You can also do it step-by-step as follows.

Step by step reproduce instructions

1. Install the whole VSR package and its requirements:

   git clone https://github.com/LoSealL/VideoSuperResolution -b ntire_2019 && cd VideoSuperResolution
   pip install -e .

   Note that you should pre-install tensorflow and pytorch.

2. Download the pre-trained model:

   *make sure you are in the root folder.

   For Real Image Super-Resolution

   python prepare_data.py --filter=rsr -q

   For sRGB Real Image Denoising (Track #2: sRGB)

   python prepare_data.py --filter=drn -q

   Model url for manually download:

   • rsr: https://github.com/LoSealL/Model/releases/download/crdn/rsr.zip
   • drn: https://github.com/LoSealL/Model/releases/download/mldn/drn.zip

3. Prepare testing data:

   *make sure you are in the root folder.

   For RSR:

   You need to crop images into small patches by:

   python VSR/Tools/DataProcessing/NTIRE19RSR.py --ref_dir=path/to/test/data/folder --patch_size=768 --stride=760 --save_dir=path/to/saving/folder

   For sRGB Denoising:

   You need to convert .MAT file to png images by:

   python VSR/Tools/DataProcessing/NTIRE19Denoise.py --validation=path/to/.MAT --save_dir=path/to/saving/folder

4. Predicting

   *make sure you are in the root folder.

   For RSR: Entering VSRTorch folder

   cd VSRTorch
   python eval.py rsr --cuda -t=/path/to/divided/test/images/folder --pth=../Results/rsr/save/rsr_ep2000.pth --ensemble

   The output will be saved in ../Results/rsr/<your-image-folder-name>. To combine them back together:

   cd ..
   python VSR/Tools/DataProcessing/NTIRE19RSR.py --ref_dir=path/to/test/data/folder --patch_size=768 --stride=760 --results=Results/rsr/<your-image-folder>/ --save_dir=path/to/saving/folder

   Where --ref_dir should keep the same as the folder in step 3, it's a reference to know how to combine patches. --patch_size and --stride should also keep the same.

   For sRGB Denoising: Entering VSRTorch folder

   cd VSRTorch
   python eval.py drn --cuda -t=/path/to/divided/test/images/folder --pth=../Results/drn/save/drn_ep2000.pth --output_index=0 --ensemble

   The output will be saved in ../Results/drn/<your-image-folder-name>. To pack them into mat file:

   cd ..
   python VSR/Tools/DataProcessing/NTIRE19Denoise.py --results=Results/drn/<your-image-folder-name> --save_dir=path/to/saving/folder

   *If OOM happened, try not to enable --cuda flag.