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.