ESPCN

A PyTorch implementation of ESPCN based on CVPR2016 paper Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Requirements

Anaconda
PyTorch

conda install pytorch torchvision -c soumith
conda install pytorch torchvision cuda80 -c soumith # install it if you have installed cuda

PyTorchNet

pip install git+https://github.com/pytorch/tnt.git@master

tqdm

pip install tqdm

opencv

conda install -c conda-forge opencv

Datasets

Train、Val Dataset

The train and val datasets are sampled from VOC2012. Train dataset has 16700 images and Val dataset has 425 images. Download the datasets from here, and then extract it into data directory. Finally run

python data_utils.py

optional arguments:
--upscale_factor      super resolution upscale factor [default value is 3]

to generate train and val datasets from VOC2012 with given upscale factors(options: 2、3、4、8).

Test Image Dataset

Test Video Dataset

The test dataset are sampled from Jay Chou's Music Videos. Download the video dataset from here, and then extract it into data/test/SRF_xx/video directory, which xx means the upscale factor.

Usage

Train

python -m visdom.server & python train.py

optional arguments:
--upscale_factor      super resolution upscale factor [default value is 3]
--num_epochs          super resolution epochs number [default value is 100]

Visdom now can be accessed by going to 127.0.0.1:8097 in your browser, or your own host address if specified.

If the above does not work, try using an SSH tunnel to your server by adding the following line to your local ~/.ssh/config : LocalForward 127.0.0.1:8097 127.0.0.1:8097.

Maybe if you are in China, you should download the static resources from here, and put them on ~/anaconda3/lib/python3.6/site-packages/visdom/static/.

Test Image

python test_image.py

optional arguments:
--upscale_factor      super resolution upscale factor [default value is 3]
--model_name          super resolution model name [default value is epoch_3_100.pt]

The output high resolution images are on results directory.

Test Video

python test_video.py

optional arguments:
--upscale_factor      super resolution upscale factor [default value is 3]
--is_real_time        super resolution real time to show [default value is False]
--delay_time          super resolution delay time to show [default value is 1]
--model_name          super resolution model name [default value is epoch_3_100.pt]

The output high resolution videos are on results directory.