linkerlin / MM-RealSR

Codes for "Metric Learning based Interactive Modulation for Real-World Super-Resolution"

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📖 MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution

arXiv
Chong Mou, Yanze Wu, Xintao Wang, Chao Dong, Jian Zhang, Ying Shan
Tencent ARC Lab; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

If you want to use the paper model, please go to this Colab Demo for MM-RealSR google colab logo.


🔧 Dependencies and Installation

Installation

  1. Clone repo

    git clone https://github.com/TencentARC/MM-RealSR.git
    cd MM-RealSR
  2. Install dependent packages

    # Install basicsr - https://github.com/xinntao/BasicSR
    # We use BasicSR for both training and inference
    pip install basicsr
    # facexlib and gfpgan are for face enhancement
    pip install facexlib
    pip install gfpgan
    pip install -r requirements.txt
    python setup.py develop

Training

python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 mmrealsr/train.py -opt options/MMRealSRNet_x4.yml --launcher pytorch --auto_resume

python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 mmrealsr/train.py -opt options/MMRealSRGAN_x4.yml --launcher pytorch --auto_resume

🏰 Model Zoo

Please download checkpoints from Google Drive or Github Release.

Testing

python inference_mmrealsr.py -opt (path to .yml file) --im_path (path to LR images) --model_path (path to checkpoint) --res_path (path to save SR images)

python Metric/LPIPS.py --folder_gt (path to HR images) --folder_restored (path to SR images)

python Metric/NIQE.py --folder_restored (path to SR images)

python Metric/DISTS.py --folder_gt (path to HR images) --folder_restored (path to SR images)

Results are in the results folder

Video Demo

Quick start can be found in Colab Demo for MM-RealSR google colab logo.

python inference_mmrealsr_demo.py -opt (path to .yml file) --model_path (path to checkpoint) --im_path (path to LR image)

The visual results of noise and blur modulation:

BibTeX

@InProceedings{mou2022mmrealsr,
    author    = {Chong Mou and Yanze Wu and Xintao Wang and Chao Dong and Jian Zhang and Ying Shan},
    title     = { Metric Learning based Interactive Modulation for Real-World Super-Resolution},
    booktitle = {European Conference on Computer Vision (ECCV)},
    date      = {2022}
}

📧 Contact

If you have any question, please email eechongm@gmail.com.

🤗 Acknowledgement

Thanks to the following open-source projects:

About

Codes for "Metric Learning based Interactive Modulation for Real-World Super-Resolution"

License:BSD 3-Clause "New" or "Revised" License


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