DeSRA (ICML 2023)

🚩 Updates

• ✅ The collected datasets, the codes of detecting artifacts and calculating metrics are released.
• ✅ Release the MSE and GAN models (Real-ESRGAN, LDL, SwinIR) and SegFormer (the checkpoint and configuration).
• ✅ Release the GAN-DeSRA models (RealESRGAN-DeSRA, LDL-DeSRA and SwinIR-DeSRA).

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This paper aims at dealing with GAN-inference artifacts.

We design a method to effectively detect regions with GAN-inference artifacts, and further propose a fine-tuning strategy that only requires a small number of artifact images to eliminate the same kinds of artifacts, which bridges the gap of applying SR algorithms to practical scenarios.

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📖 DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models

[Paper]   [Project Page]   [Video]   [B站]   [Poster]   [PPT slides]
Liangbin Xie*, Xintao Wang*, Xiangyu Chen*, Gen Li, Ying Shan, Jiantao Zhou, Chao Dong
Tencent ARC Lab; University of Macau; Shenzhen Institutes of Advanced Technology; Shanghai AI Lab

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🔧 Dependencies and Installation

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.7
• Option: NVIDIA GPU + CUDA
• Option: Linux

Installation (The version of mmsegmentation utilized in this project is 0.29.0.)

1. Install mmsegmentation package and install dependent packages. Note: The version of mmsegmentation and mmcv-full that used in the experiment are 0.29.0 and 1.6.1, respectively. Setting up the environment might take some time.

   git clone https://github.com/open-mmlab/mmsegmentation.git
   cd mmsegmentation
   pip install -r requirements.txt

2. Clone repo and move the provided scripts into the demo folder (a subfolder) in mmsegmentation folder.

   git clone https://github.com/TencentARC/DeSRA
   cd DeSRA
   mv scripts/* mmsegmentation/demo (you need to modify the path)

If you encounter problem, I also provide the environment that I used in the experiments. You can refer to the requirements.txt

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📦 Testing datasets

For three representative methods: RealESRGAN, LDL and SwinIR, we choose nearly 200 representative images with GAN-inference artifacts to construct this GAN-SR artifact dataset. You can download from GoogleDrive and BaiduDisk. (For each methods, we provide the MSE-SR, GAN-SR, DeSRA-Mask, LR, and human-labeled GT-Mask)

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🏰 Pre-trained Models

We have provided the MSE-based and GAN-based models used to detect artifacts generated by the three methods: Real-ESRGAN, LDL, and SwinIR, as well as the corresponding checkpoint and configuration files of the SegFormer we used in the experiment. You can download from GoogleDrive.

The GAN-DeSRA models are also released GoogleDrive. For each method, we have released the corresponding three checkpoints. The effect of the model fine-tuned for 1000 iterations may not be significant under some methods. In this case, you can try other checkpoints that have been fine-tuned for a longer time.

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⚔️ Quick Inference

1. Detect the artifacts between the MSE-SR results and GAN-SR results. We store many intermediate results and the final detected binary artifact map are stored in Final_Artifact_Map folder. The config file and checkpoint of SegFormer can be found in the mmsegmentation package.

   python demo/artifact_detection.py --mse_root="./LDL/MSE-SR" --gan_root="./LDL/GAN-SR" --save_root="./results/LDL/DeSRA-Mask"

2. Evaluate the performance. As mentioned in our paper, we provide three scripts to calculate IOU, Precision and Recall, respectively. You can find these scripts in metrics folder.

   python metrics/calc_iou.py
   python metrics/calc_precision.py
   python metrics/calc_recall.py

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📜 License and Acknowledgement

DeSRA is released under Apache License Version 2.0.

BibTeX

@article{xie2023desra,
    title={DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models},
    author={Xie, Liangbin and Wang, Xintao and Chen, Xiangyu and Li, Gen and Shan, Ying and Zhou, Jiantao and Dong, Chao},
    year={2023}
}

📧 Contact

If you have any question, please email lb.xie@siat.ac.cn.