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NAID

Official Code of NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset

Preparation and Dataset

  • Prerequisites
    • Python 3.x and PyTorch 1.12.
    • OpenCV, NumPy, Pillow, tqdm, lpips, einops, scikit-image and tensorboardX.
  • Dataset
    • Real-NAID dataset can be downloaded from Baidu Netdisk (password: ik9u)

Quick Start

  • Testing
    • Download pre-trained models from Baidu Netdisk (password: jcdb) and put the pre-trained checkpoint with the corresponding folder under ./ckpt/ folder
    • Download Real-NAID dataset and modify dataroot in test.sh
    • Modify name, model in test.sh and then run 
      sh test.sh
  • Training
    • Download Real-NAID dataset and modify dataroot in test.sh
    • Modify name, model and then run 
      sh train.sh
  • Note
    • You can specify which GPU to use by --gpu_ids, e.g., --gpu_ids 0, --gpu_ids 0,1, --gpu_ids -1 (for CPU mode). In the default setting, all GPUs are used.
    • You can refer to options for more arguments.

Citation

@article{xu2024nirassisted,
      title={NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset}, 
      author={Rongjian Xu and Zhilu Zhang and Renlong Wu and Wangmeng Zuo},
      journal={arXiv preprint arXiv:2404.08514},
      year={2024},
}

Acknowledement

This repo is built upon the framework of CycleGAN, and we borrow some code from Uformer, Restormer, NAFNet, thanks for their excellent work!