A Physics-Guided Dehazing Method Based on Polarization Imaging

Haze conditions degrade image quality via atmospheric scattering and absorption, posing challenges for optical imaging applications. In recent years, deep learning has emerged as an effective method for dehazing images. However, data-driven deep learning dehazing methods typically require large amounts of labeled training data and offer limited interpretability. In this paper, we propose a physics-guided dehazing method based on polarization imaging. By integrating polarization imaging with the physical model, the proposed method enables neural network training using only a set of hazy images captured at different polarization angles, thereby reducing the reliance on labeled training data. Experimental results show that the proposed method significantly outperforms commonly used deep learning dehazing methods in terms of contrast and mean gradient, while exhibiting strong generalization and physical interpretability. By integrating physical model and polarization imaging into deep learning, this method overcomes the limitations of traditional deep learning dehazing methods and paves the way for optical imaging in haze conditions.