chaoren88 / DeamNet

Adaptive Consistency Prior based Deep Network for Image Denoising (CVPR 2021, Oral)

Adaptive Consistency Prior based Deep Network for Image Denoising (CVPR 2021)

Introduction

This is the research code for the paper:

Chao Ren, Xiaohai He, Chuncheng Wang, and Zhibo Zhao, "Adaptive Consistency Prior based Deep Network for Image Denoising", CVPR 2021, oral. PDF, Supp

The proposed method achieves the state-of-the-art performance on image denoising.

Citation

If you find the code and dataset useful in your research, please consider citing:

@InProceedings{Ren_2021_CVPR,
    author    = {Ren, Chao and He, Xiaohai and Wang, Chuncheng and Zhao, Zhibo},
    title     = {Adaptive Consistency Prior Based Deep Network for Image Denoising},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8596-8606}
}

Introduction

In this paper, we propose a novel deep network for image denoising. Different from most of the existing deep network-based denoising methods, we incorporate the novel ACP term into the optimization problem, and then the optimization process is exploited to inform the deep network design by using the unfolding strategy. Our ACP-driven denoising network combines some valuable achievements of classic denoising methods and enhances its interpretability to some extent. Experimental results show the leading denoising performance of the proposed network.

We train our DeamNet for AWGN and real-world noise, and some visual results are simply shown as follows:

Visual quality comparison for ‘Starfish’ from Set12 with noise level 50

Contents

Folder	description
checkpoint	To save models when training
Dataset	Contains three folders(train, test and Benchmark_test), you can place train dataset in train, test dataset in test, and SIDD/Dnd benchmark in Benchmark_test
Deam_models	The pretrained models when you want to test
real	Some python files about real image denoising
statistics	To record the results when training

Training on AWGN

To retrain our network, please place your own training datasets in ./Dataset/train and simply run the train.py

Training on real-world noise

To retrain our network for real-world noise removal:

Download the training dataset to ./Dataset/train and use ./Dataset/train/gen_dataset_real.py to package them in the h5py format.

You can get the datasets from: https://www.eecs.yorku.ca/~kamel/sidd/dataset.php and http://ani.stat.fsu.edu/~abarbu/Renoir.html

Set the training and testing path to your own path and run train.py. For more details, please refer to https://github.com/JimmyChame/SADNet

Testing on AWGN

To test your own images, place your dataset in Dataset/test/your_test_name and run Synthetic_test.py

Testing on real-world noise

To test on real-world noisy datasets:

Download the testing dataset to ./Dataset/Benchmark_test and run Benchmark_test.py

You can get the datasets from https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php and https://noise.visinf.tu-darmstadt.de/benchmark/

Feedbacks and comments are welcome! Enjoy!