PGL-Net: Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling

Welcome to the official model repository for PGL-Net. This repository hosts the pre-trained weights and highly optimized deployment files for our efficient real-world image dehazing network.

📝 Project Overview

PGL-Net (Physics-Inspired Global-Local Decoupling Network) is a lightweight architecture that embeds physical inductive biases via operator-level emulation to address the challenging ill-posed problem of single image dehazing.

Unlike traditional methods that suffer from inaccurate parameter estimation or deep learning approaches that act as heavy "black boxes," PGL-Net explicitly decouples the dehazing process into two stages:

• Global Distribution Rectification: The Physics-Inspired Affine Fusion (PAF) module implicitly models transmission and airlight subtraction to rectify feature distributions globally.
• Local Structural Refinement: The Degradation-Aware Modulation (DAM) block adaptively restores locally variant details.

As a result, PGL-Net achieves superior restoration quality comparable to heavy Transformer models, but with only ~3% of the parameters.

• Paper: Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling
• Task: Real-World Image Dehazing
• Model Variants: PGL-Net-T (Tiny), PGL-Net-S (Small)

🚀 Deployment-Ready Weights

To facilitate real-world applications and industrial deployment, we provide extensive exported weights across multiple inference backends. All files are organized and ready for immediate download.

Available Formats

• PyTorch (.pk): Standard weights for research and fine-tuning.
• ONNX (.onnx): Cross-platform deployment.
• TensorRT (.engine): Ultra-low latency inference on NVIDIA GPUs.
• OpenVINO (.bin / .xml): Optimized for Intel CPUs/GPUs.
• MNN (.mnn): Lightweight deployment for mobile and edge devices.

Supported Datasets (Pre-trained Domains)

The weights are provided for models trained on standard real-world dehazing benchmarks:

• RUDB
• RRSHID (Remote Sensing Dehazing)
• RW2AH

(Files are named following the pattern: {dataset}_pglnet_{size}.{format})

💻 How to Download and Use

You can easily download specific deployment files using the huggingface_hub Python library.

from huggingface_hub import hf_hub_download

# Example: Download the ONNX weights for PGL-Net-T trained on RW2AH
file_path = hf_hub_download(
    repo_id="klay11/PGL-Net", 
    filename="rw2ah_pglnet_t.onnx"
)
print(f"Model downloaded to: {file_path}")

📖 Citation

If you find our work or these weights useful in your research or deployment, please consider citing our paper:

@article{qu2026efficient,
  title   = {Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling},
  author  = {Qu, Yifei and Li, Ru and Chen, Junjie and Wu, Jinyuan},
  journal = {arXiv preprint arXiv:2606.25732},
  year    = {2026}
}