# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 1 Introduction  
With the increasing severity of pollution and the gradual deterioration of the environment, haze and smog weather are characterized by a wide-ranging impact and frequent occurrences. In order to protect the environment, the inspection of the environmental quality, especially the sensing and detection of the emission of pollutants, is a problem that must be faced [1, 2]. In view of this situation, many countries have set up environmental stations to monitor companies with high pollutant emissions [3]. In haze and smog conditions, the scattering effect causes the attenuation of light transmission intensity and changes in the transmission direction, leading to a reduction in image quality obtained by optical imaging systems. Image degradation makes it difficult to effectively extract and process the information contained therein, causing certain interference in target detection and recognition [4].  
Researchers have conducted long-term and in-depth studies on image-dehazing algorithms. Image-dehazing methods are roughly divided into the following two categories: prior-based methods and learning-based methods [5]. Early image-dehazing methods have generally been based on handcraft priors and have produced images with good visibility. With the development of deep learning, in recent years, learning-based methods have dominated image dehazing. [PERSON] et al. [6] utilized the dark channel prior to obtain the transmission map for dehazing, but their results suffered from color distortion in the sky regions. Benefiting from the universality of the dark channel prior, numerous improved dehazing algorithms based on this prior have been developed subsequently. Deep learning-basedmethods like Aod-Net [7], MSCNN [8], and PSD [9] face challenges in achieving good universality due to their dependence on the training dataset.  
With the advancement of polarized light acquisition technology and polarimetric detectors, polarimetric dehazing algorithms have emerged. [PERSON] et al. [10; 11] were among the first to use rotating polarizers to capture two images of the brightest and darkest regions, estimate polarimetric parameters, and obtain dehazed images. However, these methods lack real-time performance and are not highly applicable to different scenes. [PERSON] et al. [12] utilized the optimized processing of polarimetric angles and degrees to achieve more accurate estimation of polarimetric parameters, resulting in improved dehazing performance. [PERSON] [13] applied regularization constraints to the transmission and dehazed images and achieved noise-free dehazing results through iterative processing, albeit with a longer computation time. [PERSON] [14] regularized polarimetric angles to suppress noise in the dehazed images. Nevertheless, existing polarimetric dehazing algorithms often exhibit subpar performance for distant scenes, prompting the combination of image enhancement or fusion techniques to improve dehazing quality [15; 16].  
Polarization imaging technology, as a novel electro-optical imaging technique, not only captures the two-dimensional spatial distribution of target light intensity but also obtains the target's polarization characteristics, including polarization degree, polarization angle, and polarization ellipticity. This complementary information enhances the reconnaissance and identification capabilities of ground targets [17; 18]. Moreover, combining polarization imaging technology with traditional airborne imaging detection methods offers unique advantages in highlighting targets, authenticating objects, penetrating smoke and fog, and adapting to various environments, effectively improving the detection capabilities of electro-optical platforms. Consequently, under specific weather conditions, effectively utilizing polarization information improves imaging quality and enhances contrast.