Home

Awesome

CVF-SID_PyTorch

This repository contains the official code to reproduce the results from the paper:

CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image (CVPR 2022)

[arXiv] [presentation]

<p align="center"> <img src="source/CVF.png" width="50%"/> &nbsp;&nbsp;&nbsp;&nbsp; <img src="source/framework.png" width="40%"/> </p> ## Installation Clone this repository into any place you want. ``` git clone https://github.com/Reyhanehne/CVF-SID_PyTorch.git cd CVF-SID_PyTorch ``` ### Dependencies * Python 3.8.5 * PyTorch 1.7.1 * numpy * Pillow * torchvision * scipy

Expriments

Reults of the SIDD validation dataset

<p align="center"> <img src="source/decomposition.png" width="28.5%"/> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <img src="source/augmentation.png" width="60%"/> </p> To train and evaluate the model directly please visit [SIDD](https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php) website or [Drive](https://drive.google.com/drive/folders/1cG6uCUZcBMzulkw6g9ImBOIxy_cLtiLo?usp=sharing) and download the original `Noisy sRGB data` and `Ground-truth sRGB data` from `SIDD Validation Data and Ground Truth` and place them in `data/SIDD_Small_sRGB_Only` folder.

Pretrained model

Download config.json and model_best.pth from this link and save them in models/CVF_SID/SIDD_Val/ folder.

NOTE: The pretrained model is updated at March. 9th 2022.

You can now go to src folder and test our CVF-SID by:

python test.py --device 0 --config ../models/CVF_SID/SIDD_Val/config.json --resume ../models/CVF_SID/SIDD_Val/model_best.pth

or you can train it by yourself as follows:

python train.py --device 0 --config config_SIDD_Val.json --tag SIDD_Val

Citation

If you find our code or paper useful, please consider citing:

@inproceedings{Neshatavar2022CVFSIDCM,
  title={CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image},
  author={Reyhaneh Neshatavar and Mohsen Yavartanoo and Sanghyun Son and Kyoung Mu Lee},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}