Image inpainting algorithm based on inference attention module and two-stage network

Current image inpainting techniques often yield distorted structures or blurred textures that clash with the surrounding context, particularly when addressing extensive missing regions or highly textured images. These methods often struggle to reconstruct realistic and coherent image structures. To address this challenge, we introduce a two-stage network image restoration approach that leverages an inferential attention mechanism. In the first stage, an edge generation network is employed to produce plausible phantom edge information. Subsequently, an image complementation network is utilized to complete the image restoration process. To enhance the visual realism and restoration accuracy of the generated images, we incorporate an inferential attention mechanism within the image complementation network. This mechanism effectively mitigates inconsistencies in the generated features, leading to the production of more meaningful and effective information. We evaluate our proposed method on benchmark datasets, including Places2 dataset, CelebFaces Attributes dataset, and Paris StreetView dataset. The experimental results can demonstrate that the proposed method achieves better Structural Similarity Index and Peak Signal-to-noise Ratio than others. These metrics indicate superior performance compared to existing image inpainting methods, delivering higher image inpainting accuracy and more realistic image reconstruction effect.