Image inpainting is a fundamental task in computer vision, aiming to restore missing or corrupted regions in images realistically. While recent deep learning approaches have significantly advanced the state-of-the-art, challenges remain in maintaining structural continuity and generating coherent textures, particularly in large missing areas. Diffusion models have shown promise in generating high-fidelity images but often lack the structural guidance necessary for realistic inpainting. We propose a novel inpainting method that combines diffusion models with anisotropic Gaussian splatting to capture both local structures and global context effectively. By modeling missing regions using anisotropic Gaussian functions that adapt to local image gradients, our approach provides structural guidance to the diffusion-based inpainting network. The Gaussian splat maps are integrated into the diffusion process, enhancing the model's ability to generate high-fidelity and structurally coherent inpainting results. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques, producing visually plausible results with enhanced structural integrity and texture realism.

本研究解决了图像修复中在大型缺失区域保持结构连续性和生成一致纹理的挑战。我们提出了一种结合扩散模型与各向异性高斯涂抹的新型修复方法，通过适应局部图像梯度的高斯函数对缺失区域进行建模，为基于扩散的修复网络提供结构指导。实验表明，该方法在实现高保真和结构一致的修复效果方面优于现有技术。

基于各向异性高斯涂抹的扩散模型图像修复