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GAN based super-resolution (presented at ECCV 2018)
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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

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

Usage for testing

  • Set valid paths for testset and trained models in
  • Run

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
  • 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
  • Download vgg 19 model in here
  • Run (takes a few days)
  • Run


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}


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