1 Super Resolution

1.1 Principle

Super resolution is a process of upscaling and improving the details within an image. It usually takes a low-resolution image as input and upscales the same image to a higher resolution as output. Here we provide three super-resolution models, namely RealSR, ESRGAN, LESRCNN. RealSR proposed a realworld super-resolution model aiming at better perception. ESRGAN is an enhanced SRGAN that improves the three key components of SRGAN. LESRCNN is a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks.

1.2 How to use

1.2.1 Prepare Datasets

A list of common image super-resolution datasets is as following:

Name	Datasets	Short Description	Download
2K Resolution	DIV2K	proposed in NTIRE17 (800 train and 100 validation)	official website
Classical SR Testing	Set5	Set5 test dataset	Google Drive / Baidu Drive
Classical SR Testing	Set14	Set14 test dataset	Google Drive / Baidu Drive

The structure of DIV2K is as following:

  DIV2K
     ├── DIV2K_train_HR
     ├── DIV2K_train_LR_bicubic
     |    ├──X2
     |    ├──X3
     |    └──X4
     ├── DIV2K_valid_HR
     ├── DIV2K_valid_LR_bicubic
     ...

The structures of Set5 and Set14 are similar. Taking Set5 as an example, the structure is as following:

  Set5
    ├── GTmod12
    ├── LRbicx2
    ├── LRbicx3
    ├── LRbicx4
    └── original

1.2.2 Train/Test

Datasets used in example is df2k, you can change it to your own dataset in the config file. The model used in example is RealSR, you can change other models by replacing the config file.

Train a model:

   python -u tools/main.py --config-file configs/realsr_bicubic_noise_x4_df2k.yaml

Test the model:

   python tools/main.py --config-file configs/realsr_bicubic_noise_x4_df2k.yaml --evaluate-only --load ${PATH_OF_WEIGHT}

1.3 Results

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

The metrics are PSNR / SSIM.

Method	Set5	Set14	DIV2K
realsr_df2k	28.4385 / 0.8106	24.7424 / 0.6678	26.7306 / 0.7512
realsr_dped	20.2421 / 0.6158	19.3775 / 0.5259	20.5976 / 0.6051
realsr_merge	24.8315 / 0.7030	23.0393 / 0.5986	24.8510 / 0.6856
lesrcnn_x4	31.9476 / 0.8909	28.4110 / 0.7770	30.231 / 0.8326
esrgan_psnr_x4	32.5512 / 0.8991	28.8114 / 0.7871	30.7565 / 0.8449
esrgan_x4	28.7647 / 0.8187	25.0065 / 0.6762	26.9013 / 0.7542

1.4 模型下载

模型	数据集	下载地址
realsr_df2k	df2k	realsr_df2k
realsr_dped	dped	realsr_dped
realsr_merge	DIV2K	realsr_merge
lesrcnn_x4	DIV2K	lesrcnn_x4
esrgan_psnr_x4	DIV2K	esrgan_psnr_x4
esrgan_x4	DIV2K	esrgan_x4

References

• 1. Real-World Super-Resolution via Kernel Estimation and Noise Injection

  @inproceedings{ji2020real,
  title={Real-World Super-Resolution via Kernel Estimation and Noise Injection},
  author={Ji, Xiaozhong and Cao, Yun and Tai, Ying and Wang, Chengjie and Li, Jilin and Huang, Feiyue},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={466--467},
  year={2020}
  }
  

• 2. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

  @inproceedings{wang2018esrgan,
  title={Esrgan: Enhanced super-resolution generative adversarial networks},
  author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={0--0},
  year={2018}
  }
  

• 3. Lightweight image super-resolution with enhanced CNN

  @article{tian2020lightweight,
  title={Lightweight image super-resolution with enhanced CNN},
  author={Tian, Chunwei and Zhuge, Ruibin and Wu, Zhihao and Xu, Yong and Zuo, Wangmeng and Chen, Chen and Lin, Chia-Wen},
  journal={Knowledge-Based Systems},
  volume={205},
  pages={106235},
  year={2020},
  publisher={Elsevier}
  }