PyTorch implementation for Multi-Scale Adaptive Network for Single Image Denoising (NeurIPS 2022).

Requirements

• Python 3.6.13
• Pytorch 1.9.0
• mmcv 1.3.14
• h5py, pillow, numpy, scikit-image, etc.

Testing

First, for testing on real noise images, please organize each test dataset as follows

|--test_dataset
|   |--clean
|   |   |--*.png
|   |--noise
|   |   |--*.png

and run test.py through

python test.py \
        --real \                                # flag of real noise images
        --save_result \                         # flag of saving the denoised results
        --ckpt_pth ckpt/real_with_jpeg.pth \    # path to models
        --data_root dataset/test/ \             # path to datasets
        --datasets "['Nam_PNG']"                # list of datasets

For testing on synthetic noise images, please organize each test dataset as follows

|--test_dataset
|   |--clean
|   |   |--*.png
|   |--sig30
|   |   |--*.png
|   |--sig50
|   |   |--*.png
|   |--sig70
|   |   |--*.png

and run test.py for color noise image denoising through

python test.py \
        --save_result \                         # flag of saving the denoised results
        --sigma 30 \                            # noise level
        --ckpt_pth ckpt/color_sig30.pth \       # path to models
        --data_root dataset/test/ \             # path to datasets
        --datasets "['CMcMaster']"              # list of datasets

as well as grayscale noise image denoising through

python test.py \
        --gray  \                               # flag of grayscale noise images
        --save_result \                         # flag of saving the denoised results
        --sigma 30 \                            # noise level
        --ckpt_pth ckpt/gray_sig30.pth \        # path to models
        --data_root dataset/test/ \             # path to datasets
        --datasets "['GMcMaster']"              # list of datasets

Training

To train your own models, please modify the arguments in the train.py and run it through

python train.py

Citation

If this work is helpful, please cite it, thanks! >_<

@inproceedings{msanet,
  title={Multi-Scale Adaptive Network for Single Image Denoising},
  author={Yuanbiao Gou and Peng Hu and Jiancheng Lv and Joey Tianyi Zhou and Xi Peng},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022}
}

Acknowledgement

This work uses some packages from mmcv in the implementation, thanks for their excellent work!