---
license: cc-by-nc-sa-4.0
---
# Resfusion

Official model weights for **Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise**.

This repository hosts pretrained checkpoints intended for **image restoration** tasks, including:

- Shadow removal
- Low-light image enhancement
- Raindrop removal

If you use this model or repository in research, please cite the original paper.

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## Overview

Resfusion is a diffusion-based image restoration framework that incorporates **prior residual noise patterns** into the denoising diffusion process. Compared with standard diffusion-based restoration pipelines, Resfusion is designed to better exploit degradation-aware residual structure for higher-quality restoration.

This Hugging Face repository is intended to provide:

- Pretrained model weights
- Basic task descriptions
- Inference guidance
- Links to the original codebase and paper

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### Intended uses

This model is intended for research and experimental applications in:

- Image restoration benchmarking
- Shadow removal
- Low-light enhancement
- Raindrop removal
- Diffusion-based restoration studies

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## Training data

The original implementation supports the following datasets:

- **ISTD** for shadow removal
- **LOL** for low-light image enhancement
- **Raindrop** for raindrop removal

Please refer to the original project for exact data preparation, folder structure, and training configuration.

---

## Usage

### Option 1: Use with the official codebase

Please use the official Resfusion repository for inference and evaluation:

- **Project repository:** `https://github.com/nkicsl/Resfusion`
- **arXiv link:** `https://arxiv.org/abs/2311.14900`
- **NeurIPS poster page:** `https://nips.cc/virtual/2024/poster/95696`

Typical workflow:

1. Clone the official repository.
2. Create the environment from `environment.yaml`.
3. Download the checkpoint from this Hugging Face repository.
4. Place the checkpoint in your preferred checkpoint directory.
5. Run the corresponding test script for your task.

---

## Citation

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

```bibtex
@inproceedings{NEURIPS2024_ebc62a3a,
 author = {Shi, Zhenning and zheng, haoshuai and Xu, Chen and Dong, Changsheng and Pan, Bin and xueshuo, Xie and He, Along and Li, Tao and Fu, Huazhu},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
 pages = {130664--130693},
 publisher = {Curran Associates, Inc.},
 title = {Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise},
 url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/ebc62a3af9342eb4ebc728e5c5bc4cca-Paper-Conference.pdf},
 volume = {37},
 year = {2024}
}
```