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Invertible Image Denoising

This is the PyTorch implementation of paper: Invertible Denoising Network: A Light Solution for Real Noise Removal (CVPR 2021). arxiv.

Dependencies and Installation

Python 3 (Recommend to use Anaconda)
PyTorch >= 1.0
NVIDIA GPU + CUDA
Python packages: pip install numpy opencv-python lmdb pyyaml
TensorBoard:
- PyTorch >= 1.1: pip install tb-nightly future
- PyTorch == 1.0: pip install tensorboardX

Dataset Preparation

The datasets used in this paper is DND (can be downloaded here), SIDD (can be downloaded here) and RNI.

Get Started

Training and testing codes are in 'codes/'. Please see 'codes/README.md' for basic usages. Pretrained model can be found in 'pretrained/'

Invertible Architecture

Visual Results

Qualitative results on the SIDD, DND and RNI dataset All visual results for SIDD dataset can be found in 'results/'.

Acknowledgement

The code is based on Invertible Image Rescaling. If you find this code is helpful, please also cite the paper Invertible Image Rescaling.

Citation

If you find this work helps you, please cite:

@article{liu2021invertible,
  title={Invertible Denoising Network: A Light Solution for Real Noise Removal},
  author={Liu, Yang and Qin, Zhenyue and Anwar, Saeed and Ji, Pan and Kim, Dongwoo and Caldwell, Sabrina and Gedeon, Tom},
  journal={arXiv preprint arXiv:2104.10546},
  year={2021}
}

Contact

If you have any questions, please contact yang.liu3@anu.edu.au or zhenyue.qin@anu.edu.au.