Awesome

VDN

Variational Denoising Network: Toward Blind Noise Modeling and Removal (NeurIPS, 2019, paper)

Note that this work has been extended to a general image restoration method (paper, code).

NeurIPS 2019 reproducibility challenge project

• Report

Requirements and Dependencies

• Ubuntu 16.04, cuda 10.0
• Python 3.6, Pytorch 1.1.0
• More detail (See environment.yml)

Image Denoising

Non-IID Gauss Noise Removal

In the training stage, we used the source images in Waterloo, CBSD432 and CImageNet400 as groundtruth, and the variance map of the noise was generated with Gaussian kernel. However, in the test stage, three different variance maps were adopted to generate noisy images to verify the generalization of VDN.

Testing

    python demo_test_simulation.py

Training

1. Download the source images from the above links.
2. Prepare the testing datasets:

    python datasets/prepare_data/simulation/noise_generate_nips_niid.py

3. Begining training:

    python train_simulation.py --simulate_dir source_imgs_path --eps2 5e-5

Real-world Noise Removal

The real-world VDN denoiser was trained on the SIDD Medium Dataset, and directly tested on the SIDD and DND benchmarks.

Testing:

    python demo_test_benchmark.py

Training:

1. Download the training and validation(noisy, groundtruth) datasets, and put the unzipped training dataset and the validation dataset into the floder "sidd_data_path".

2. Writing the training and validation datasets into hdf5 fromat:

    python datasets/prepare_data/SIDD/big2small_train.py --data_dir sidd_data_path
    python datasets/prepare_data/SIDD/big2small_test.py --data_dir sidd_data_path

3. Begin training:

    python train_benchmark.py --SIDD_dir sidd_data_path --eps2 1e-6

Citation

@incollection{NIPS2019_8446,
title = {Variational Denoising Network: Toward Blind Noise Modeling and Removal},
author = {Yue, Zongsheng and Yong, Hongwei and Zhao, Qian and Meng, Deyu and Zhang, Lei},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {1690--1701},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8446-variational-denoising-network-toward-blind-noise-modeling-and-removal.pdf}
}