To address the challenges of restoring details in heavily occluded areas and enhancing network generalization capabilities in occluded polarized image reconstruction tasks, the research proposes a novel occluded image reconstruction model, PolarReconGAN, based on multiscale adversarial network. The proposed model integrates with polarization array imaging technology, aims to reconstruct the polarization information of occluded targets, thereby improving image quality and detail representation. We design a multiscale feature extraction module that employs a random window slicing method to prevent information loss due to image resizing, and utilizes data augmentation to enhance model generalization. Additionally, a loss function based on discrete wavelet transform is employed to further improve the reconstruction effects of image details. The experimental results demonstrate that the proposed method achieves an average structural similarity index (SSIM) of 0.7720 and an average peak signal-to-noise ratio (PSNR) of 25.2494 dB on a multi-view occluded polarization image dataset, indicating superior performance in occluded image reconstruction.