Efficient Hierarchical Feature Collaboration Transformer for Image Inpainting
Dengyong Zhang, Nuo Fu, Xin Liao, Jiaxin Chen, Hengfu Yang, Gaobo Yang · IEEE Transactions on Multimedia · 2025
Existing image inpainting methods face limitations in detail restoration. Although transformer-based models have made certain progress recently, the lack of hierarchical feature interaction and insufficient consideration of the importance of features at different network levels lead to semantic ambiguity in image reconstruction. To enhance the visual quality and accuracy of image inpainting, we adopt a multi-level feature fusion approach and propose a novel, efficient hierarchical feature collaboration transformer (HFCT). Our approach comprises two modules: dual stream gated feature fusion (DSGF) and region-separated attention module (RSAM), effectively capturing features at different levels of the network and enhancing inter-level information exchange. The DSGF module uses soft gating to fuse primary and advanced features, strengthening the connection from local to global consistency and reducing artifacts. The RSAM module resolves attention isolation issues in feature fusion through region-separated attention, strengthening the understanding of feature relationships, capturing more image semantics, and improving restoration accuracy. Extensive experiments on the Paris StreetView, CelebA-HQ, and Places2 benchmark datasets demonstrate that our proposed method achieves superior image inpainting quality compared to several state-of-the-art inpainting algorithms.