<p>Dunhuang murals are afflicted by complex degradation phenomena, including extensive content loss, blurred textures, and pervasive mould-spot corrosion. Existing image inpainting methods often fail to handle these intricate degradation patterns effectively, primarily due to their limited ability to model long-range dependencies, susceptibility to structural flow noise, and inability to preserve high-frequency details. To address these challenges, we propose ST-GVMNet, a Structure-Texture Dual-Stream Network that synergistically integrates Gating Mechanisms with the Visual Mamba (V-Mamba) architecture for Dunhuang mural inpainting. Adopting a coarse-to-fine, decoupled strategy, the network consists of two core components: a Structure Reconstruction Network (SRN) and a Texture Refinement Network (TRN). In the SRN stage, we develop a Structure Gate Fusion Module (SGFM) to adaptively filter structural features and suppress edge noise through spatially weighted gating operations. For texture inpainting, we introduce a Multi-scale Dilated VMamba (MD-VMamba) module, which leverages 2D selective scanning and dilated feedforward blocks to capture both global contextual information and fine-grained local details. Furthermore, a Variance-Gated Aggregation Module (VGAM) is proposed to dynamically recover high-frequency nuances by utilizing local variance as a saliency indicator. Extensive experiments on the Dunhuang mural and Thangka datasets, as well as standard benchmarks, demonstrate that ST-GVMNet achieves state-of-the-art (SOTA) performance in both quantitative metrics and qualitative visual fidelity. Notably, our method exhibits exceptional structural coherence and robustness when inpainting images under extreme mask ratios.</p>