Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model
_{Official PyTorch Implementation of DiffUIR.}

Project Page | Paper | Personal HomePage

Updates

[2024.12.02] Added onedrive checkpoint link.
[2024.09.17] More instruction was given for test.py and visual.py for better usage.
[2024.03.18] The 14 datasets link is released.
[2024.03.17] The whole training and testing codes are released!!!
[2024.03.16] The 4 versions of pretrained weights of DiffUIR are released.
[2024.02.27] 🎉🎉🎉 Our DiffUIR paper was accepted by CVPR 2024 🎉🎉🎉

Introduction

The main challenge of universal image restoration tasks is handling different degradation images at once. In this work, we propose a selective hourglass mapping strategy based on conditional diffusion model to learn the shared information between different tasks. Specifically, we integrate a shared distribution term to the diffusion algorithm elegantly and naturally, achieving to map the different distributions to a shared one and could further guide the shared distribution to the task-specific clean image. By only modify the mapping strategy, we outperform large-scale universal methods with at least five times less computational costing.

Framework comparison

How to use

Environment

Python 3.79
Pytorch 1.12

Install

Create a virtual environment and activate it.

conda create -n diffuir python=3.7
conda activate diffuir

Dependencies

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch -c nvidia
pip install opencv-python
pip install scikit-image
pip install tensorboard
pip install matplotlib 
pip install tqdm

Dataset Preparation

Preparing the train and test datasets following our paper Dataset Construction section as:

Datasets/Restoration
|--syn_rain
|  |--train
      |--input
      |--target
|  |--test
|--Snow100K
|--Deblur
|--LOL
|--RESIDE
   |--OTS_ALPHA
      |--haze
      |--clear
   |--SOTS/outdoor
|--real_rain
|--real_dark
|--UDC_val_test

Then get into the data/universal_dataset.py file and modify the dataset paths.

Dataset Links

train

Degradation	blurry	low-light	rainy	snowy	hazy
Datasets	Gopro	LOL	syn_rain	Snow100K	RESIDE(5vss)

test

Degradation	blurry	low-light	rainy	snowy	hazy
Datasets	gopro+HIDE+real_R+real_J	LOL	combine	Snow100K	SOTS

real

Degradation	real_dark	real_rain	UDC	real_snow
Datasets	dark	rain	UDC	Snow100K

Train

We train the five image restoration tasks at once, you can change train.py-Line42 to change the task type.
Note that the result of our paper can be reimplemented on RTX 4090, using 3090 or other gpus may cause performance drop.

python train.py

Test and Calculate the Metric

Note that the dataset of SOTS can not calculate the metric online as the number of input and gt images is different. Please use eval/SOTS.m.
The pretrained weight of model-300.pt is used to test with timestep 3, check double times whether you loaded, the ``result_folder'' !!!.
Notably, change the 'task' id in test.py Line 43 to your task, low-light enhancement for 'light_only', deblur for 'blur', dehaze for 'fog', derain for 'rain', desnow for 'snow'

python test.py

For Under-Camera real-world dataset, as the resolution is high, we split the image into several pathes and merge them after model.
The pretrained weight of model-130.pt is used to test with timestep 4.

python test_udc.py

Visualize

Here you can test our model in your personal image. Note that if you want to test low-light image, please use the code src/visualization-Line1286-1288

python visual.py

Model Zoo (Google drive)

Model	DiffUIR-T (0.89M)	DiffUIR-S (3.27M)	DiffUIR-B (12.41M)	DiffUIR-L (36.26M)
Arch	32-1111 Tiny	32-1224 Small	64-1224 Base	64-1248 Large

We provide different model versions of our DiffUIR, you can replace the code in train.py Line 58-65 with the model size mentioned above(64-1224)

Model Zoo (Onedrive)

Model	DiffUIR-T (0.89M)	DiffUIR-S (3.27M)	DiffUIR-B (12.41M)	DiffUIR-L (36.26M)
Arch	32-1111 Tiny	32-1224 Small	64-1224 Base	64-1248 Large

Qualitative results on five restoration tasks

Qualitative results on real-world images

Analysis of the shared information

The distribution before and after our SDT, SDT map the different distributions to a shared one.

The attention of the feature map, our method could focus on the degradation type (rain and fog), validating that we learn the useful shared information.

Citation

If you find this project helpful in your research, welcome to cite the paper.

@inproceedings{zheng2024selective,
  title={Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model},
  author={Zheng, Dian and Wu, Xiao-Ming and Yang, Shuzhou and Zhang, Jian and Hu, Jian-Fang and Zheng, Wei-shi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

Acknowledgements

Thanks to Jiawei Liu for opening source of his excellent works RDDM. Our work is inspired by these works and part of codes are migrated from RDDM.

Contact

Please contact Dian Zheng if there is any question (1423606603@qq.com or zhengd35@mail2.sysu.edu.cn).

iSEE-Laboratory/DiffUIR

Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model Official PyTorch Implementation of DiffUIR.