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Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise

arxiv link: https://arxiv.org/abs/2311.14900

neurips poster page: https://nips.cc/virtual/2024/poster/95696

This repository is the official Pytorch Lightning implementation for Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise.

Directory Structure

1. callback mainly used to store the implementation of EMA as a callback.
2. datamodule mainly used for storing datamodules for different datasets.
3. eval mainly stores code for evaluation.
4. model mainly stores the main Resfusion models and its denoising backbones.
5. .py starting with train are for training, while those starting with test are for testing.

Environment Setup

conda env create -f environment.yaml

Dataset Download

ISTD

LOL

Raindrop

Please download them to the datasets directory and organize them as follows:

├── resfusion-master
├── datasets
    ├── ISTD
        ├── train
        ├── test
    ├── LOLdataset
        ├── our485
        ├── eval15
    ├── Raindrop
        ├── train
        ├── test_a

Training Pipeline

ISTD Dataset

python train_resfusion_restore_mask.py --num_workers 24 --T 12 --batch_size 4 --device 8 --denoising_model RDDM_Unet 

LOL Dataset

python train_resfusion_restore.py --num_workers 24 --T 12 --denoising_model RDDM_Unet

Raindrop Dataset

python train_resfusion_restore.py --T 12 --dataset Raindrop --data_dir ../datasets/Raindrop --batch_size 4 --device 8

CIFAR10 Dataset (example with 100 sampling steps)

python train_resfusion_generate.py --T 273 --num_workers 24 --batch_size 128 --devices 1 --blr 4e-4 --min_lr 2e-4 --use_ema

Testing Pipeline

Step 1: Run the testing script (taking ISTD dataset as an example)

ISTD dataset

python test_resfusion_restore_mask.py --T 12 --model_ckpt ./ckpt/ISTD/best-epoch\=2639-val_PSNR\=30.068.ckpt --seed 42

Step 2: Export generated prediction images using ./eval/save_images_for_test.ipynb

Step 3: Align the names of exported prediction images with real test dataset images using ./eval/name_alignment.ipynb

Step 4: Assess quantitative metrics using MATLAB files and .py files in ./eval

Results Download

Estimation of Parameters and MACs

Consistent with RDDM, we used THOP to assess the parameters and MACs, see the code in ./eval/cal_params_and_macs.py

Truncated Strategy

We have provided a mapping table acc_T_change_table.xlsx between $T'$ and $T$ in our truncated schedule, along with the corresponding curve graph for $\sqrt{\overline{\alpha}_{t}}$.

Tips

1. Strictly adhere to the hyperparameters set during training when testing the model.

Thanks

Thanks to MulimgViewer for the support in generating visual comparison results, and special thanks to @ObscureLin for the technique support throughout the project.

Citation

If you find this work useful for your research, please consider citing:

@article{shi2023resfusion,
  title={Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise},
  author={Shi, Zhenning and Zheng, Haoshuai and Xu, Chen and Dong, Changsheng and Pan, Bin and Xie, Xueshuo and He, Along and Li, Tao and Fu, Huazhu},
  journal={arXiv preprint arXiv:2311.14900},
  year={2023}
}

License

Shield:

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.