🚀 PolarFree: Polarization-based Reflection-Free Imaging – [CVPR 2025]

🌟 A Cutting-Edge Solution and Dataset for Polarization-based Reflection-Free Imaging

Image source: ThinkLucid

🔗 Project Page | 📄 Paper | 📦 [Dataset](Coming soon...)

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📌 Highlights

✅ Large-Scale Dataset: PolaRGB includes 6,500 well-aligned RGB-polarization image pairs, 8× larger than existing datasets.
✅ Innovative Method: PolarFree leverages diffusion models to generate reflection-free priors for accurate reflection removal.
✅ State-of-the-Art Performance: Outperforms existing methods by ~2dB in PSNR on challenging real-world scenarios.
✅ Open Source: Code and dataset are freely available for research and development.

⏳ Timeline

• ✅ 2025-03-23 - 🛠️ Repository initialized with documentation.
• ✅ 2025-03-23 - 🔗 Project Page officially launched.
• ⬜ TODO: 📄 Paper available on arXiv.
• ⬜ TODO: 🚀 Provide core codebase and pre-trained models for evaluation.
• ⬜ TODO: 📦 Release the full PolaRGB dataset with download links.
• ⬜ TODO: 📝 Publish training code and instructions.

📖 Overview

PolarFree addresses the challenging task of reflection removal using polarization cues and a novel diffusion-based approach. Key contributions include:

• PolaRGB Dataset: A large-scale dataset with diverse indoor and outdoor scenes, providing RGB and polarization images.

• Diffusion Model: Utilizes diffusion processes to generate reflection-free priors, enabling precise reflection removal and improved image clarity.
  

• Superior Results: Extensive experiments on the PolaRGB dataset show that PolarFree outperforms existing methods by ~2dB in PSNR, achieving cleaner reflection removal and sharper image details.

• Real-World Effectiveness: PolarFree demonstrates robust performance in real-world scenarios, such as museums and galleries, effectively reducing reflections while preserving fine details.

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🚀 Installation

1. Clone the repository:

   git clone https://github.com/mdyao/PolarFree.git
   cd PolarFree
   pip install -r requirements.txt

2. Run the demo:

   python demo.py --input example.jpg --output result.jpg
   

📊 Results

PolarFree achieves superior performance compared to existing methods:

📜 Citation

If you find this work useful, please cite:

@inproceedings{polarfree2025,
title={PolarFree: Polarization-based Reflection-Free Imaging},
author={Mingde Yao, Menglu Wang, King-Man Tam, Lingen Li, Tianfan Xue, Jinwei Gu},
booktitle={CVPR},
year={2025}
}