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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Introduction

@inproceedings{ledig2016photo,
  title={Photo-realistic single image super-resolution using a generative adversarial network},
  author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
  year={2016}
}

Results and Models

Evaluated on RGB channels, scale pixels in each border are cropped before evaluation.

The metrics are PSNR / SSIM.

Method Set5 Set14 DIV2K Download
msrresnet_x4c64b16_1x16_300k_div2k 30.2252 / 0.8491 26.7762 / 0.7369 28.9748 / 0.8178 model | log
srgan_x4c64b16_1x16_1000k_div2k 27.9499 / 0.7846 24.7383 / 0.6491 26.5697 / 0.7365 model | log