Polarization-based dehazing algorithm under dense hazy conditions

The scattering of light induced by hazy conditions severely degrades imaging quality, leading to reduced contrast, loss of detail, and blurring. These issues present severe challenges to critical fields such as autonomous driving, remote sensing, and object detection. Traditional Stokes vector-based dehazing algorithms are particularly susceptible to noise interference in polarized images, which considerably hampers their dehazing performance under dense hazy conditions. To address these limitations, this paper proposes a polarization-based dehazing approach that integrates the PBM3D denoising algorithm with the Stokes vector. This method effectively separates scattered light from target reflected light while suppressing noise and enhancing image details and clarity. Experimental results demonstrate that in real-world dense hazy conditions, the proposed method significantly outperforms traditional Stokes vector-based dehazing algorithms. This study not only enhances imaging quality in hazy conditions but also lays a foundation for future applications in extreme hazy conditions across domains such as autonomous driving and environmental monitoring.