  Image inpainting, or image completion, is a crucial task in computer vision
that aims to restore missing or damaged regions of images with semantically
coherent content. This technique requires a precise balance of local texture
replication and global contextual understanding to ensure the restored image
integrates seamlessly with its surroundings. Traditional methods using
Convolutional Neural Networks (CNNs) are effective at capturing local patterns
but often struggle with broader contextual relationships due to the limited
receptive fields. Recent advancements have incorporated transformers,
leveraging their ability to understand global interactions. However, these
methods face computational inefficiencies and struggle to maintain fine-grained
details. To overcome these challenges, we introduce MxT composed of the
proposed Hybrid Module (HM), which combines Mamba with the transformer in a
synergistic manner. Mamba is adept at efficiently processing long sequences
with linear computational costs, making it an ideal complement to the
transformer for handling long-scale data interactions. Our HM facilitates
dual-level interaction learning at both pixel and patch levels, greatly
enhancing the model to reconstruct images with high quality and contextual
accuracy. We evaluate MxT on the widely-used CelebA-HQ and Places2-standard
datasets, where it consistently outperformed existing state-of-the-art methods.
