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Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising

The source code for paper "Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising" (CVPR 2023)

Usage

Datasets

Download SIDD and DND datasets, and modify dataset_path in dataset/base.py accordingly.

|- dataset_path
  |- SIDD
    |- SIDD_Medium_Srgb
      |- Data
        |- 0001_001_S6_00100_00060_3200_L
        |- 0002_001_S6_00100_00020_3200_N
        |- ...
    |- SIDD_Validation
      |- ValidationNoisyBlocksSrgb.mat
      |- ValidationGtBlocksSrgb.mat
    |- SIDD_Benchmark
      |- BenchmarkNoisyBlocksSrgb.mat
  |- DND
    |- info.mat
    |- images_srgb

Validation

Validate on SIDD Validation dataset,

cd validate
python validate_SIDD.py

Training (removed due to confidentiality agreement, see here)

Training on SIDD Medium dataset,

sh train.sh

Citation

If you find our work useful in your research or publication, please cite:

@inproceedings{li2023spatially,
  title={Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising},
  author={Li, Junyi and Zhang, Zhilu and Liu, Xiaoyu and Feng, Chaoyu and Wang, Xiaotao and Lei, Lei and Zuo, Wangmeng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2023}
}