Serial section electron microscopy (ssEM), an advanced three-dimensional imaging technique, has played a crucial role in studying neuronal connections and microstructures of the brain. However, imperfect sample preparation and image acquisition randomly cause degradation of ssEM images, where Missing Slices (MS) is the most significant one. The previous deep learning methods such as interpolation model using spatially-adaptive convolutions have been proven to outperform conventional approaches, but those methods fail to recover some high-frequency information of missing slices. In this work, we propose a novel approach leveraging Denoising Diffusion Probabilistic Models (DDPM) for MS restoration in ssEM images. Our method improves the U-Net backbone with asymmetric and symmetric 3D convolutions, adapting to the anisotropic characteristic of ssEM images. Then, we introduce the Adaptive and Learnable Reconstruction module (ALR) and First and Last slices Attention Block (FLAB) to extract the features of slices effectively. Moreover, we employ a diffusion correction method, reducing training-testing data discrepancies. Experiment results demonstrate the effectiveness of our method in generating realistic missing slices, surpassing previous methods.