SGCM-Net: structure-guided mural inpainting with state space model

The degraded regions of ancient murals often contain intricate textures and structural curves, presenting major challenges for traditional mural restoration. Although digital image inpainting offers a viable approach, Convolutional Neural Network (CNN)-based methods are commonly constrained by these complex scenarios and struggle with global consistency due to their limited receptive fields. To address these limitations, a CNN-Mamba hybrid inpainting architecture is proposed based on the two-stage task decomposition paradigm. This framework employs Structure-Guided Fusion Blocks (SGFBs) to adaptively fuse structural priors from the edge inpainting stage across multi-scale levels. To enhance holistic consistency, the proposed Multi-Way Mamba Process Blocks (MMPBs) are integrated into the bottleneck, specifically adapting State Space Models (SSMs) to capture global relations in 2D murals with linear complexity. Comprehensive evaluations on mural and landscape painting datasets show that the proposed method properly restores global styles, fills in coherent details, and achieves competitive performance compared to well-established methods.