电子测量与仪器学报

鲍昌皓,高欣健,王文莉,王昕,高隽.结合高频感知的大气偏振模式生成方法[J].电子测量与仪器学报,2024,38(4):18-26

结合高频感知的大气偏振模式生成方法

Atmospheric polarization mode generation method basedon high-frequency perception

DOI：

英文关键词:atmospheric polarization mode polarization information reconstruction high frequency perception soft segmentation and soft synthesis

基金项目:国家自然科学基金（62171178）项目资助

作者	单位
鲍昌皓	1.合肥工业大学计算机与信息学院合肥230009；2.合肥工业大学图像信息处理研究室合肥230009
高欣健	合肥工业大学计算机与信息学院合肥230009
王文莉	1.合肥工业大学计算机与信息学院合肥230009；2.合肥工业大学图像信息处理研究室合肥230009
王昕	1.合肥工业大学计算机与信息学院合肥230009；2.合肥工业大学图像信息处理研究室合肥230009
高隽	1.合肥工业大学计算机与信息学院合肥230009；2.合肥工业大学图像信息处理研究室合肥230009

Author	Institution
Bao Changhao	1.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China; 2.Image Information Processing Laboratory, Hefei University of Technology, Hefei 230009, China
Gao Xinjian	School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
Wang Wenli	1.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China; 2.Image Information Processing Laboratory, Hefei University of Technology, Hefei 230009, China
Wang Xin	1.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China; 2.Image Information Processing Laboratory, Hefei University of Technology, Hefei 230009, China
Gao Jun	1.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China; 2.Image Information Processing Laboratory, Hefei University of Technology, Hefei 230009, China

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中文摘要:

大气偏振模式是一种稳定的自然属性，其在导航、探测等领域有广泛的应用，但由于自然环境以及周边建筑物遮挡的影响，在同一时刻获取的大气偏振信息是局部且不连续的，导致其在实际应用中受到影响。现有方法主要对大气偏振模式进行大范围图像的修复，对于高频信号的修复精度十分有限导致边缘模糊。针对该问题，本文采用软分割软合成的方法，对偏振信息进行冗余分割并合成，避免了高频信号的丢失，挖掘每个局部中的高频信号特征，并根据大气偏振模式时空连续性合理推测，保证重构的信息与真实信息维持一致，从而生成完整连续的大气偏振信息。实验结果证明，本方法能够很好地重构大气偏振模式中缺失的偏振信息，在云层干扰大于40%的实测重构实验中，本文方法的SSIM和PSNR得分相较于其他方法提高了26%和12%。

英文摘要:

Atmospheric polarization mode are stable natural attributes with widespread applications in navigation, detection, and other fields. However, due to the influence of natural environments and surrounding structures, the polarization information obtained at the same time is often local and discontinuous, impacting its practical use. Existing methods mainly focus on repairing large-scale images of atmospheric polarization mode, resulting in limited accuracy in restoring high-frequency signals and causing edge blurring. To address this issue, this paper proposes a method of soft segmentation and synthesis for polarization information, which avoids the loss of high-frequency signals by redundantly segmenting and synthesizing the polarization information, thereby mining the high-frequency signal features in each local region. Additionally, based on the spatiotemporal continuity of atmospheric polarization mode, reasonable inference is made to ensure consistency between the reconstructed information and the real information, thereby generating complete and continuous atmospheric polarization information. Experimental results demonstrate that this method effectively reconstructs missing polarization information in atmospheric polarization mode. In practical reconstruction experiments where cloud interference exceeds 40%, the proposed method shows a 26% improvement in SSIM and a 12% improvement in PSNR compared to other methods.

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