README.md

[ISCAS 2022] SUNet: Swin Transformer with UNet for Image Denoising

Chi-Mao Fan, Tsung-Jung Liu, Kuan-Hsien Liu

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Abstract : Image restoration is a challenging ill-posed problem which also has been a long-standing issue. In the past few years, the convolution neural networks (CNNs) almost dominated the computer vision and had achieved considerable success in different levels of vision tasks including image restoration. However, recently the Swin Transformer-based model also shows impressive performance, even surpasses the CNN-based methods to become the state-of-the-art on high-level vision tasks. In this paper, we proposed a restoration model called SUNet which uses the Swin Transformer layer as our basic block and then is applied to UNet architecture for image denoising.

Network Architecture

Overall Framework of SUNet
Swin Transformer Layer	Dual up-sample

Quick Run

You can directly run personal noised images on my space of HuggingFce.

To test the pre-trained models of denoising on your own 256x256 images, run

python demo.py --input_dir images_folder_path --result_dir save_images_here --weights path_to_models

Here is an example command:

python demo.py --input_dir './demo_samples/' --result_dir './demo_results' --weights './pretrained_model/denoising_model.pth'

To test the pre-trained models of denoising on your arbitrary resolution images, run

python demo_any_resolution.py --input_dir images_folder_path --stride shifted_window_stride --result_dir save_images_here --weights path_to_models

SUNset could only handle the fixed size input which the resolution in training phase same as the mostly transformer-based methods because of the attention masks are fixed. If we want to denoise the arbitrary resolution input, the shifted-window method will be applied to avoid border effect. The code of demo_any_resolution.py is supported to fix the problem.

Train

To train the restoration models of Denoising. You should check the following components:

• training.yaml:

    # Training configuration
    GPU: [0,1,2,3] 
  
    VERBOSE: False
  
    SWINUNET:
      IMG_SIZE: 256
      PATCH_SIZE: 4
      WIN_SIZE: 8
      EMB_DIM: 96
      DEPTH_EN: [8, 8, 8, 8]
      HEAD_NUM: [8, 8, 8, 8]
      MLP_RATIO: 4.0
      QKV_BIAS: True
      QK_SCALE: 8
      DROP_RATE: 0.
      ATTN_DROP_RATE: 0.
      DROP_PATH_RATE: 0.1
      APE: False
      PATCH_NORM: True
      USE_CHECKPOINTS: False
      FINAL_UPSAMPLE: 'Dual up-sample'
  
    MODEL:
      MODE: 'Denoising'
  
    # Optimization arguments.
    OPTIM:
      BATCH: 4
      EPOCHS: 500
      # EPOCH_DECAY: [10]
      LR_INITIAL: 2e-4
      LR_MIN: 1e-6
      # BETA1: 0.9
  
    TRAINING:
      VAL_AFTER_EVERY: 1
      RESUME: False
      TRAIN_PS: 256
      VAL_PS: 256
      TRAIN_DIR: './datasets/Denoising_DIV2K/train'       # path to training data
      VAL_DIR: './datasets/Denoising_DIV2K/test' # path to validation data
      SAVE_DIR: './checkpoints'           # path to save models and images
  

• Dataset:
  The preparation of dataset in more detail, see datasets/README.md.

• Train:
  If the above path and data are all correctly setting, just simply run:

  python train.py
  

Result

Visual Comparison

Citation

If you use SUNet, please consider citing:

@inproceedings{fan2022sunet,
  title={SUNet: swin transformer UNet for image denoising},
  author={Fan, Chi-Mao and Liu, Tsung-Jung and Liu, Kuan-Hsien},
  booktitle={2022 IEEE International Symposium on Circuits and Systems (ISCAS)},
  pages={2333--2337},
  year={2022},
  organization={IEEE}
}