Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images

Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, Jianzhuang Liu

Abstract: In the last few years, image denoising has benefited a lot from the fast development of neural networks. However, the requirement of large amounts of noisy-clean image pairs for supervision limits the wide use of these models. Although there have been a few attempts in training an image denoising model with only single noisy images, existing self-supervised denoising approaches suffer from inefficient network training, loss of useful information, or dependence on noise modeling. In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images. Firstly, a random neighbor sub-sampler is proposed for the generation of training image pairs. In detail, input and target used to train a network are images sub-sampled from the same noisy image, satisfying the requirement that paired pixels of paired images are neighbors and have very similar appearance with each other. Secondly, a denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance. The proposed Neighbor2Neighbor framework is able to enjoy the progress of state-of-the-art supervised denoising networks in network architecture design. Moreover, it avoids heavy dependence on the assumption of the noise distribution. We explain our approach from a theoretical perspective and further validate it through extensive experiments, including synthetic experiments with different noise distributions in sRGB space and real-world experiments on a denoising benchmark dataset in raw-RGB space.

Official Pytorch implementation for the paper accepted by CVPR 2021.

Resources

• Arxiv
• Conference
• Supplementary

Python Requirements

This code was tested on:

• Python 3.7
• Pytorch 1.3

Preparing Training Dataset

Images in the training set are from the ImageNet validation set with size between 256x256 and 512x512 pixels. There are 44328 images in total.

python dataset_tool.py 
--input_dir=./ILSVRC2012_img_val 
--save_dir=./Imagenet_val

• optional arguments:
  • input_dir Path to the ImageNet validation set
  • save_dir Path to save the training set

Training

To train a network, run:

python train.py 
--data_dir=./Imagenet_val 
--val_dirs=./validation 
--noisetype=gauss25 
--save_model_path=./results 
--log_name=unet_gauss25_b4e100r02 
--increase_ratio=2

• selected optional arguments:
  • data_dir Path to the training set
  • val_dirs Path to the validation sets
  • noisetype Distribution of image noise, choosing from gauss25, gauss5_50, poisson30, or poisson5_50
  • save_model_path Base-path to the saved files
  • log_name Path to the saved files
  • increase_ratio Weight for the trade off between the reconstruction term and the regularization term in the loss function

Citations

@InProceedings{Huang_2021_CVPR,
    author    = {Huang, Tao and Li, Songjiang and Jia, Xu and Lu, Huchuan and Liu, Jianzhuang},
    title     = {Neighbor2Neighbor: Self-Supervised Denoising From Single Noisy Images},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {14781-14790}
}