Face and Image Super-resolution

Paper

Adrian Bulat*, Jing Yang*, Georgios Tzimiropoulos ''To learn image super-resolution, use a GAN to learn how to do image degradation first'' in ECCV2018

Method

High-to-Low GAN using unpaired low and high-resolution images to simulate the image degradation
Low-to-High GAN using paired low and high-resolution images to learn real-world super resolution
GAN loss driving the image generation process

Requirements

Pytorch 0.4.1

Data

Trainset is in Dataset. HIGH is the training high resolution images. LOW is the training low resolution images
Testset is testset.tar
test_res.tar is our result

Running testing

CUDA_VISIBLE_DEVICES=0, python model_evaluation.py

Fid Calculation

CUDA_VISIBLE_DEVICES=0, python fid_score.py /Dataset/HIGH/SRtrainset_2/ test_res/

This code is from https://github.com/mseitzer/pytorch-fid

Citation

@inproceedings{bulat2018learn, 
  title={To learn image super-resolution, use a GAN to learn how to do image degradation first},
  author={Bulat, Adrian and Yang, Jing and Tzimiropoulos, Georgios},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={185--200},
  year={2018}  
}

License

This project is licensed under the MIT License

haolyshiit / Face-and-Image-super-resolution

Face and Image Super-resolution

Paper

Method

Requirements

Data

Running testing

Fid Calculation

Citation

License

简介

发行版

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Face and Image Super-resolution

Paper

Method

Requirements

Data

Running testing

Fid Calculation

Citation

License

简介

发行版

贡献者

近期动态

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haolyshiit / Face-and-Image-super-resolution