Dehazing Algorithm Based on Joint Polarimetric Transmittance Estimation via Multi-Scale Segmentation and Fusion

• Published in: Applied sciences Vol. 15; no. 15; p. 8632
• Main Authors: Wang, Zhen, Zhang, Zhenduo, Cao, Xueying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Algorithms; Deep learning; Fog; Light; Methods; multi-scale segmentation-fusion; Neural networks; polarimetric dehazing; Sensors; transmittance estimation
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15158632

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.