Image inpainting aims to fill in the missing regions of damaged images with plausible content. Existing inpainting methods tend to produce ambiguous artifacts and implausible structures. To address the above issues, our method aims to fully utilize the information of known regions to provide style and structural guidance for missing regions. Specifically, the Adaptive Style Fusion (ASF) module reduces artifacts by transferring visual style features from known regions to missing regions. The Gradient Attention Guidance (GAG) module generates accurate structures by aggregating semantic information along gradient boundary regions. In addition, the Multi-scale Attentional Feature Extraction (MAFE) module extracts global contextual information and enhances the representation of image features. The sufficient experimental results on the three datasets demonstrate that our proposed method has superior performance in terms of visual plausibility and structural consistency compared to state-of-the-art inpainting methods.
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