# A Polarization-Based Method for Maritime Image Dehazing
###### Abstract  
The accurate identification of target imagery in the presence of sea fog is essential for the precise detection and comprehension of targets situated at sea. To overcome the issues encountered when applying traditional polarimetric dehazing methods to sea fog imagery, this paper proposes an improved polarimetric dehazing method. Initially, the methodology employs quartile-based selection on polarization difference images to ascertain atmospheric light at an infinite distance. Subsequently, the study describes a segmentation approach for sea-sky background images based on the degree of polarization. The results show that the image information entropy of the segmentation process improves by more than 6% compared to that of alternative methodologies, and the local contrast of the image is increased by more than 30% compared to that of the original foggy image. These outcomes confirm the effectiveness of the proposed dehazing methodology in addressing the challenges associated with sea fog imagery. polarimetric imaging; image dehazing; image segmentation; sea fog +
Footnote †: journal:their minimal equipment requirements, adaptability, consistent dehazing results, and cost-effectiveness [11]. [PERSON] et al. [7] proposed the classic polarized-difference dehazing method, which involves capturing \"brightest\" and \"darkest\" intensity images by rotating a linear polarizer in front of the camera lens. By combining their assumptions with physical models, the researchers achieve haze-free image restoration. [PERSON] et al. [5] suggested using Stokes vectors to calculate the angle of polarization (AOP) of scattered light and deriving the air light from this angle to generate a clear, haze-free image using a physical model. By estimating the angle of polarization for the air light, noise in the image is effectively reduced, enhancing the dehazing capability. Building on this work, [PERSON] et al. [12; 13; 14] incorporated filtering methods to explore air light denoising, yielding positive results. In the context of estimating the atmospheric light intensity at infinity (referred to as \(A_{\infty}\)), various methodologies have been proposed in the literature. [PERSON] et al. [15; 16] have suggested an approach that involves identifying two points in an image with identical pixel values under clear weather conditions, but this method faces challenges in maritime environments where targets are often small. [PERSON] et al. [17] introduced a technique to estimate \(A_{\infty}\), even in the absence of sky background conditions, by transforming formulas, although this method requires complex computations and may not be practical for sea fog images with extensive sky coverage. Some strategies incorporate the dark channel prior principle for \(A_{\infty}\) detection [6; 18], but in maritime settings, the presence of large sky regions can lead to high pixel values across all channels, reducing the effectiveness of this principle [19; 20]. Additionally, conventional polarimetric dehazing techniques may cause sea regions to appear black in dehazed images when dealing with objects that act as specular reflectors, resulting in an overestimation of the air light [21; 22]. [PERSON] [21] proposed a correction method to address this issue, suggesting the linear interpolation of air light near specular objects and using the adjusted air light for these objects. [PERSON] [13] presented an alternative method for handling specular objects with small areas and high \(A_{p}\), representing the polarized radiance of the air light, by setting a threshold based on the average \(A_{p}\) value and adjusting values exceeding this threshold to a slightly lower fixed value. While these approaches are suitable for scenarios where specular objects are a minor part of the image, their utility may be constrained in marine settings. The majority of the polarimetric dehazing techniques previously discussed have been developed to reduce fog in terrestrial settings, with only a few methods focusing on sea fog.