Owing to their compact structure, high stability, and low cost, Indirect Time-of-Fligh (IToF) cameras have gained increasing attention in the fields of robotics and automation. However, in real-world scenarios, IToF cameras are affected by multipath interference, which severely degrades imaging quality. Existing learning-based methods for multipath interference correction are all based on CNN architectures and rely on synthetic datasets, leading to poor generalization in real-world scenarios. We proposed an efficient and accurate real data collection scheme and explored the application of Transformer and Mamba in multipath interference correction tasks. Additionally, we introduced a cross-propagation network that integrates Mamba and CNN modules, reducing system complexity to linear levels while achieving superior multipath interference correction compared to state-of-the-art methods.