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.

Polarized 3D imaging technology reconstructs the three-dimensional (3D) surface shape of an object by analyzing the polarization characteristics of light reflected from its surface. A key challenge in polarized 3D imaging is accurately estimating the zenith angle. Specular light poses a notable challenge in estimating the zenith angle because it conveys limited information regarding the target. To enhance the accuracy and robustness of zenith angle estimation for specular light, this study proposes a novel zenith angle estimation method that utilizes both specular and diffuse reflections. Based on the estimated zenith angle, the target surface shape was reconstructed. The feasibility of the proposed method was validated using polarimetric images of marine targets, offering a new solution for the accurate identification and 3D imaging of distant maritime targets.

To address the significant degradation of image visibility and contrast in turbid media, this paper proposes an enhanced image dehazing algorithm. Unlike traditional polarimetric dehazing methods that exclusively attribute polarization information to airlight, our approach integrates object radiance polarization and airlight polarization for haze removal. First, sky regions are localized through multi-scale fusion of polarization and intensity segmentation maps. Second, region-specific transmittance estimation is performed by differentiating haze-occluded regions from haze-free regions. Finally, target radiance is solved using boundary constraints derived from non-haze regions. Compared with other dehazing algorithms, the method proposed in this paper demonstrates greater adaptability across diverse scenarios. It achieves higher-quality restoration of targets with results that more closely resemble natural appearances, avoiding noticeable distortion. Not only does it deliver excellent dehazing performance for land fog scenes, but it also effectively handles maritime fog environments.

To address the issue of reduced image contrast and visibility caused by turbid media, such as dense fog, this paper proposes a novel polarization-based single-image dehazing model. The model introduces a first-of-its-kind nonlinear joint polarization model for airlight and target light. This model is established within a Cartesian coordinate system, abstracted as an analytical geometric model. Leveraging the structural similarity principle in images, boundary constraints are applied to enhance the accuracy of target light estimation. Finally, image dehazing and enhancement are achieved using the atmospheric scattering model. Experimental results demonstrate that the proposed algorithm does not rely on dataset training, maintains the highest structural consistency, and achieves superior image restoration across various scenarios, producing results that most closely resemble natural observation.

With the continuous advancement of polarization spectral sensing technology, multi-band polarization image fusion has emerged as a novel approach to image fusion. By integrating spectral and polarization information, this method overcomes the limitations of relying on a single information source and significantly improves overall image quality. To address this, this paper proposes a new polarization spectral fusion algorithm. First, feature matching is employed to achieve pixel-level spatial alignment of multi-band polarization images. Then, a fusion strategy based on multi-scale decomposition and singular value decomposition is adopted to preserve structural information and fine details. Subsequently, fractional-order processing and guided filtering are applied to enhance details and suppress noise. Finally, a progressive reconstruction from low to high scales is performed to ensure hierarchical consistency and information integrity throughout the fusion process. In addition, spectral information is utilized for color restoration, enabling the final image to achieve high spatial resolution while maintaining natural and rich color representation.Experimental results demonstrate that the proposed method effectively integrates features from different spectral bands and polarization information while preserving maximum similarity, leading to significant improvements in both image quality and detail representation.

Marine oil spills require reliable quantitative characterization to support accurate detection and thickness retrieval in remote sensing applications. However, existing optical approaches often treat thin-film interference, polarization, and extinction as weakly coupled processes, limiting physical interpretability across oil types and thickness ranges. This study develops a polarized thin-film optical model for air-oil-water interfaces by explicitly coupling dual-beam interference, polarization, and experimentally constrained wavelength-dependent extinction. Laboratory measurements demonstrate that oil-layer extinction exhibits pronounced nonlinear spectral dependence that cannot be captured by conventional simplified formulations. Incorporating this experimentally constrained extinction into the polarized interference framework reveals distinct interference- and extinction-dominated optical regimes, with enhanced sensitivity to thin oil films in the ultraviolet-blue spectral region. For thicknesses below ∼50 μm, coherent thin-film interference dominates, producing pronounced wavelength-dependent oscillations, whereas at greater thicknesses increasing extinction within the oil layer suppresses secondary reflections and drives polarization signatures toward saturation. The model further demonstrates that the degree of linear polarization (DOLP) shows opposite monotonic trends with increasing thickness at incident angles of 35° and 65°, highlighting the intrinsic geometry dependence of polarization-based retrievals. Comparisons with laboratory measurements at 488-590 nm show excellent agreement, with near-zero mean residuals (|μ| = 0.0001). These results establish a physically rigorous and experimentally validated framework for polarization-based remote sensing of marine oil films.