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Residual Dense Network for Image Super-Resolution

Introduction

@inproceedings{zhang2018residual,
  title={Residual dense network for image super-resolution},
  author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2472--2481},
  year={2018}
}

Results

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

The metrics are PSNR / SSIM.

Method Set5 Set14 DIV2K Download
rdn_x2c64b16_g1_1000k_div2k 35.9883 / 0.9385 31.8366 / 0.8920 34.9392 / 0.9380 model | log
rdn_x3c64b16_g1_1000k_div2k 32.6051 / 0.8943 28.6338 / 0.8077 31.2153 / 0.8763 model | log
rdn_x4c64b16_g1_1000k_div2k 30.4922 / 0.8548 26.9570 / 0.7423 29.1925 / 0.8233 model | log