Adaptive Dynamic Filtering Network for Image Denoising (AAAI 2023)

Abstract: In image denoising networks, feature scaling is widely used to enlarge the receptive field size and reduce computational costs. This practice, however, also leads to the loss of high-frequency information and fails to consider within-scale characteristics. Recently, dynamic convolution has exhibited powerful capabilities in processing high-frequency information (e.g., edges, corners, textures), but previous works lack sufficient spatial contextual information in filter generation. To alleviate these issues, we propose to employ dynamic convolution to improve the learning of high-frequency and multi-scale features. Specifically, we design a spatially enhanced kernel generation (SEKG) module to improve dynamic convolution, enabling the learning of spatial context information with a very low computational complexity. Based on the SEKG module, we propose a dynamic convolution block (DCB) and a multi-scale dynamic convolution block (MDCB). The former enhances the high-frequency information via dynamic convolution and preserves low-frequency information via skip connections. The latter utilizes shared adaptive dynamic kernels and the idea of dilated convolution to achieve efficient multi-scale feature extraction. The proposed multi-dimension feature integration (MFI) mechanism further fuses the multi-scale features, providing precise and contextually enriched feature representations. Finally, we build an efficient denoising network with the proposed DCB and MDCB, named ADFNet. It achieves better performance with low computational complexity on real-world and synthetic Gaussian noisy datasets.

Network Architecture (click to expand)

Environment

• Python 3.6 + Pytorch 1.0 + CUDA 10.1
• numpy
• skimage
• imageio
• cv2

Get Started

The Deformable ConvNets V2 (DCNv2) module in our code adopts chengdazhi's implementation.

You can compile the code according to your machine.

sh make.sh

Please make sure your machine has a GPU, which is required for the DCNv2 module.

Training

RGB image denoising

• Download DIV2K training data (800 training images) to train ADFNet or Download DIV2K+Flickr2K+BSD400+WED to train ADFNet*.

• for ADFNet

  Run bash train_adfnet_n10.sh or bash train_adfnet_n30.shor bash train_adfnet_n50.sh or bash train_adfnet_n70.sh

• for ADFNet*

  Run bash train_adfnet-L_n50.sh

Gray image denoising

• Run bash train_adfnet_n15.sh or bash train_adfnet_n25.shor bash train_adfnet_n50.sh

Real-world image denoising

• Download the SIDD-Medium dataset from here

• Generate image patches

  python generate_patches_SIDD.py --ps 256 --num_patches 300 --num_cores 10

  and then place all image patches in ./datasets/sidd_patch

• Download validation images of SIDD and place them in ./testsets/sidd/val

• Install warmup scheduler

• Train your model with default arguments by running

  Run bash train_adfnet.sh

Evaluation

Part of pre-trained models: Google drive or Baidu cloud

RGB image denoising

• cd ./ADFNet_RGB

• Download models and place it in ./checkpoints

• Download testsets (CBSD68, Kodak24, McMaster) and place it in ./testsets

• Run python test.py --save_images --chop

Gray image denoising

• cd ./ADFNet_Gray

• Download models and place it in ./checkpoints

• Download testsets (BSD68, Urban100, Set12) and place it in ./testsets

• Run python test.py --save_images --chop

Real-world image denoising

• cd ./ADFNet_Real

• Download the model and place it in ./pretrained_models

​ Testing on SIDD datasets

• Download sRGB validation images of SIDD and place them in ./datasets/sidd/val

• First, run test_sidd_val_png.py --save_images --ensemble,

  and then run evaluate_SIDD.m to calculate the PSNR/SSIM value

• Download benchmark BenchmarkNoisyBlocksSrgb.mat of SIDD and place them in ./datasets/sidd/benchmark

• Run test_sidd_benchmark_mat.py --save_images --ensemble

​ Testing on DND datasets

• Download sRGB images of DND and place them in ./datasets/dnd/
• Run test_dnd_png.py --save_images --ensemble

Citation

If you use ADFNet, please consider citing:

@article{shen2022adaptive,
  title={Adaptive Dynamic Filtering Network for Image Denoising},
  author={Shen, Hao and Zhao, Zhong-Qiu and Zhang, Wandi},
  booktitle={AAAI},
  year={2023}
}

Acknowledgment: This code is based on the MIRNet and DAGL toolbox.