轻量实时去雾系统，无需显卡也能流畅运行
Hazedefy: A Lightweight Real-Time Image and Video Dehazing Pipeline for Practical Deployment
- 基于暗通道先验与大气散射模型，设计简化计算流程
- 支持实时视频和摄像头流，实测提升画面清晰度与对比度
- 适合手机、嵌入式设备部署，可不依赖GPU
本文提出Hazedefy，一种面向实际部署的轻量级实时图像与视频去雾流水线，专为消费级硬件优化。该方案基于暗通道先验（DCP）与大气散射模型，引入伽马自适应重建、带下界约束的快速透射率近似、基于分数顶部像素平均的稳定大气光估计，以及可选的颜色平衡阶段。实验在真实图像与视频上验证了其有效性，显著改善可见度与对比度，且无需GPU加速即可实现流畅处理，适用于移动与嵌入式场景。
原文摘要 · Abstract (English)
This paper introduces Hazedefy, a lightweight and application-focused dehazing pipeline intended for real-time video and live camera feed enhancement. Hazedefy prioritizes computational simplicity and practical deployability on consumer-grade hardware, building upon the Dark Channel Prior (DCP) concept and the atmospheric scattering model. Key elements include gamma-adaptive reconstruction, a fast transmission approximation with lower bounds for numerical stability, a stabilized atmospheric light estimator based on fractional top-pixel averaging, and an optional color balance stage. The pipeline is suitable for mobile and embedded applications, as experimental demonstrations on real-world images and videos show improved visibility and contrast without requiring GPU acceleration.
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