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| license: cc-by-nc-sa-4.0 |
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| # Resfusion |
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| Official model weights for **Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise**. |
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| This repository hosts pretrained checkpoints intended for **image restoration** tasks, including: |
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| - Shadow removal |
| - Low-light image enhancement |
| - Raindrop removal |
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| If you use this model or repository in research, please cite the original paper. |
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| ## Overview |
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| 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. |
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| This Hugging Face repository is intended to provide: |
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| - Pretrained model weights |
| - Basic task descriptions |
| - Inference guidance |
| - Links to the original codebase and paper |
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| ### Intended uses |
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| This model is intended for research and experimental applications in: |
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| - Image restoration benchmarking |
| - Shadow removal |
| - Low-light enhancement |
| - Raindrop removal |
| - Diffusion-based restoration studies |
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| ## Training data |
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| The original implementation supports the following datasets: |
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| - **ISTD** for shadow removal |
| - **LOL** for low-light image enhancement |
| - **Raindrop** for raindrop removal |
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| Please refer to the original project for exact data preparation, folder structure, and training configuration. |
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| ## Usage |
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| ### Option 1: Use with the official codebase |
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| Please use the official Resfusion repository for inference and evaluation: |
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| - **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` |
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| Typical workflow: |
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| 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. |
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| ## Citation |
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| If you find this work useful for your research, please consider citing:: |
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| ```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} |
| } |
| ``` |
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