To achieve efficient denoising of polarization images without losing their original polarization characteristics, this paper proposes a polarization image denoising algorithm based on masked pre-training. The algorithm first performs pre-training on a clear polarization image dataset by applying masks, using the parameters obtained from the pre-trained model as initial parameters for self-supervised masked training. Noisy polarization images are then input for iterative training. During each iteration, predictions for the masked pixels are extracted, and an exponential moving average strategy is employed to generate the final prediction of the clean image. Experimental results demonstrate that the proposed algorithm not only achieves direct denoising of polarization images but also delivers superior visual performance in restoring polarization information. Additionally, its robustness is validated across different noise patterns and noise levels.