neighbor2neighbor 代码 debug完，参考

# 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](https://arxiv.org/pdf/2101.02824.pdf) - [Conference](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Neighbor2Neighbor_Self-Supervised_Denoising_From_Single_Noisy_Images_CVPR_2021_paper.pdf) - [Supplementary](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Neighbor2Neighbor_Self-Supervised_Denoising_CVPR_2021_supplemental.pdf) ## 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. ```bash 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: ```bash 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} } ```