---
language:
- en
license: mit
pipeline_tag: image-to-image
library_name: pytorch
tags:
- image-dehazing
- spiking-neural-network
- computer-vision
- haze-removal
- image-restoration
- pytorch_model_hub_mixin
- model_hub_mixin
- safetensors
arxiv: 2512.23950
---

# DehazeSNN

> **U-Net-Like Spiking Neural Networks for Single Image Dehazing**

[[📄 Paper](https://huggingface.co/papers/2512.23950)] [[💻 GitHub](https://github.com/HaoranLiu507/DehazeSNN)]

DehazeSNN integrates a U-Net-like encoder-decoder architecture with Spiking Neural Networks (SNNs), using an Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) for efficient cross-channel communication. This yields competitive dehazing quality with fewer parameters and MACs compared to Transformer-based methods.

## Available Checkpoints

| Revision | Size | Dataset | Load Command |
|----------|------|---------|--------------|
| `main` | L | RESIDE-ITS | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN")` |
| `l-reside-its` | L | RESIDE-ITS | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="l-reside-its")` |
| `l-reside-ots` | L | RESIDE-OTS | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="l-reside-ots")` |
| `l-reside-6k` | L | RESIDE-6k | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="l-reside-6k")` |
| `l-rs-haze` | L | RS-Haze | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="l-rs-haze")` |
| `m-reside-its` | M | RESIDE-ITS | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="m-reside-its")` |
| `m-reside-6k` | M | RESIDE-6k | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="m-reside-6k")` |
| `m-rs-haze` | M | RS-Haze | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="m-rs-haze")` |

## Quick Start

### Installation

```bash
# Clone the DehazeSNN repository (for model code + custom CUDA kernels)
git clone https://github.com/HaoranLiu507/DehazeSNN.git
cd DehazeSNN

# Create environment (requires CUDA 12.x)
conda create -n DehazeSNN python=3.11 -y
conda activate DehazeSNN

# Install PyTorch with CUDA 12.1
conda install pytorch=2.1.2 torchvision pytorch-cuda=12.1 -c pytorch -c nvidia -y

# Install dependencies
pip install huggingface_hub safetensors cupy-cuda12x timm
```

### Load and Run Inference

```python
import torch
from PIL import Image
import numpy as np

# Import from the cloned repository
from models.hub import DehazeSNNHub

# Load model (default: DehazeSNN-L trained on RESIDE-ITS)
model = DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN")
model.cuda().eval()

# Load a different variant
# model = DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="m-reside-6k")

# Prepare input image (RGB, normalized to [-1, 1])
img = Image.open("hazy_image.jpg").convert("RGB")
img_np = np.array(img).astype(np.float32) / 255.0
img_tensor = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0)  # [1, 3, H, W]
img_tensor = (img_tensor - 0.5) / 0.5  # normalize to [-1, 1]
img_tensor = img_tensor.cuda()

# Inference
with torch.no_grad():
    output = model(img_tensor).clamp(-1, 1)

# Convert output back to image
output = (output * 0.5 + 0.5).squeeze(0).permute(1, 2, 0).cpu().numpy()
output = (output * 255).astype(np.uint8)
Image.fromarray(output).save("dehazed_image.jpg")
```

## Requirements

- **CUDA GPU required**: The custom LIF CUDA kernels require an NVIDIA GPU with CUDA support.
- **CuPy**: `cupy-cuda12x` (for CUDA 12.x) - CPU-only inference is **not supported**.
- PyTorch >= 2.1 with CUDA 12.1.
- Python 3.11 recommended.

## Model Sizes

| Variant | Depths | Parameters |
|---------|--------|------------|
| M (Medium) | [8, 12, 16, 12, 8] | 2.70M |
| L (Large) | [8, 16, 32, 16, 8] | 4.75M |

## Citation

If you find this work useful, please cite our paper:

```bibtex
@INPROCEEDINGS{11228727,
  author={Li, Huibin and Liu, Haoran and Liu, Mingzhe and Xiao, Yulong and Li, Peng and Zan, Guibin},
  booktitle={2025 International Joint Conference on Neural Networks (IJCNN)}, 
  title={U-Net-Like Spiking Neural Networks for Single Image Dehazing}, 
  year={2025},
  pages={1-9},
  doi={10.1109/IJCNN64981.2025.11228727}
}
```

## License

This project is released under the MIT License.