/EnhanceNet-PyTorch

A PyTorch implementation of EnhanceNet (ENET) for Single Image Super Resolution (SISR)

Primary LanguagePythonMIT LicenseMIT

EnhanceNet-PyTorch

A PyTorch implementation of ENET-PA for Single Image Super Resolution (SISR).

Screenshot

Example from ENET paper

If you use this architecture in your work please cite the original paper:

@inproceedings{enhancenet,
  title={{EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis}},
  author={Sajjadi, Mehdi S. M. and Sch{\"o}lkopf, Bernhard and Hirsch, Michael},
  booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
  pages={4501--4510},
  year={2017},
  organization={IEEE},
  url={https://arxiv.org/abs/1612.07919/}
}

Description

ENET-PA here is implemented in PyTorch as there is no current implementation in PyTorch. All credit goes to Sajjad et al. Adversarial learning along with perceptual loss (hence P+A). The model is in the form of a GAN and does 4x upscaling of 64x64 images to 512x512.

https://arxiv.org/abs/1612.07919/

TODO

  • Add texture matching loss (ENET-PAT)
  • Make user friendly for out-of-box train and test