SRFeat: Single Image Super-Resolution with Feature Discrimination

This is the implementation of the models and source code for the "SRFeat: Single Image Super-Resolution with Feature Discrimination", ECCV2018. [project]

File description

• config.py: inlucde configuration for file paths and hyper parameters for networks
• main_gan_init.py: code to pretrain our generator with MSE loss
• main_gan_train.py: code to train our generator with VGG loss, image GAN loss, feature GAN loss
• main_gan_eval.py: code to infernce our generator with a trained model.
• models/SRFeat_init.npz: weight model trained by main_gan_init.py
• models/SRFeat_full.npz: weight model trained by main_gan_train.py

Usage for testing

• Set valid paths for testset and trained models in config.py
• Run main_gan_eval.py

Usage for training

Because directly training a network with GAN loss is difficult, we first pretrain our network with MSE loss and after train our network with GAN loss. We uploaded matlab codes for data augmentation described in the paper. In the default, we use about 100,000 pre-cropped LR-HR patches made from DIV2K dataset. We recommend to precompute LR images by MATLAB imresize function ('bicubic' option) for getting the same performance in the paper when you want to use other dataset.

• Download DIV2K dataset from https://data.vision.ee.ethz.ch/cvl/DIV2K/
• Run data_augmentation/aug_data_div2k.m and data_augmentation/aug_data_div2k_half.m to augment the dataset.
• Set valid paths for the augmented dataset and validation set in config.py
• Download vgg 19 model in here
• Run main_gan_init.py (takes a few days)
• Run main_gan_train.py

Citation

@InProceedings{Park_2018_ECCV,
author = {Park, Seong-Jin and Son, Hyeongseok and Cho, Sunghyun and Hong, Ki-Sang and Lee, Seungyong},
title = {SRFeat: Single Image Super-Resolution with Feature Discrimination},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2018}
}

License

This software is being made available under the terms in the LICENSE file.

Any exemptions to these terms requires a license from the Pohang University of Science and Technology.

About Coupe Project

Project ‘COUPE’ aims to develop software that evaluates and improves the quality of images and videos based on big visual data. To achieve the goal, we extract sharpness, color, composition features from images and develop technologies for restoring and improving by using it. In addition,ersonalization technology through userreference analysis is under study.

Please checkout out other Coupe repositories in our Posgraph github organization.

Acknowledgement

• Code architecture is based on tensorlayer-srgan