## 4 Conclusions  
This paper introduces a sea surface glare adaptive polarization suppression method based on the geographic polarization suppression model. Initially, the real-time positioning information solution method and the real-time orientation information solution method within the geographic suppression model were detailed. Subsequently, experimental validations were conducted for the real-time positioning information glare suppression method, the real-time orientation information glare suppression method, and the glare suppression method using the geographic polarization model. Moreover, a pixel normalization method was introduced to further suppress glare. The experimental results demonstrate that the method proposed in this paper exhibits effective glare suppression under various conditions. Specifically, the average relative error for the real-time positioning information glare suppression method is 5.91%; for the real-time orientation information glare suppression method, it is 1.7%; and for the glare suppression method using the geographic polarization model, it is 11.81%. Additionally, the SSIM image quality assessment method was employed to evaluate the images following glare suppression, showing that the proposed method maintains a high similarity in image quality, with SSIM values above 0.8 compared to the original images without glare. This indicates high similarity and low discrepancy between the suppressed and original non-glare images. In summary, the method proposed in this paper effectively suppresses the rapidly changing sea surface glare, achieving all-time adaptive and stable suppression of sea surface glare, providing significant assurance and support for the field of marine monitoring. The sea surface glare suppression method proposed in this paper plays a significant role in improving the accuracy and quality of passive optical imaging of the ocean surface, and has also had a notable impact on the advancement of other disciplines. This method holds considerable application potential in fields such as marine remote sensing observation, climate change research, marine resource utilization, maritime security monitoring, and drone surveillance. In marine remote sensing, our method can effectively suppress the interference of sea surface glare, providing high-resolution remote sensing images to aid in the assessment of marine ecological changes and pollution levels, offering important guidance for exploration activities involving submarine oil, gas, and deep-sea mineral resources. In climate change research, our method can enhance the precision of monitoring key climate indicators such as sea surface temperature and sea ice coverage, providing technical support for the study of major climate phenomena such as global warming and sea-level rise. In the field of maritime security monitoring, our method can significantly improve the quality of surveillance images, enhancing the safety of maritime operations. For drone monitoring systems, our method can reduce glare interference and improve monitoring accuracy. In summary, the research results of this paper not only contribute to multiple disciplinary fields at a technical level, but also lay a solid foundation for the practical application and development of these fields, having profound significance for progress in environmental protection, climate change research, resource development, maritime safety, and military reconnaissance. In future research, our initial plan is to address the issue of the slow response of adaptive dynamic polarization control under extreme weather conditions. Specifically, we intend to employ end-to-end deep learning techniques to enhance the performance of the glare suppression algorithm as well as the response speed and efficiency of dynamic polarization control. We will design and train a deep learning network that will be trained on a dataset of glare images.