Inpainting, for filling missing image regions, is a crucial task in various applications, such as medical imaging and remote sensing. Trending data-driven approaches efficiency, for image inpainting, often requires extensive data preprocessing. In this sense, there is still a need for model-driven approaches in case of application constrained with data availability and quality, especially for those related for time series forecasting using image inpainting techniques. This paper proposes an improved modeldriven approach relying on patch-based techniques. Our approach deviates from the standard Sum of Squared Differences (SSD) similarity measure by introducing a Hybrid Similarity (HySim), which combines both strengths of Chebychev and Minkowski distances. This hybridization enhances patch selection, leading to high-quality inpainting results with reduced mismatch errors. Experimental results proved the effectiveness of our approach against other model-driven techniques, such as diffusion or patch-based approaches, showcasing its effectiveness in achieving visually pleasing restorations.

本研究提出了一种基于基于补丁技术的改进型模型驱动方法，通过引入混合相似度（HySim）来增强补丁选择，从而实现了高质量的图像修复成果。实验结果证明，与其他模型驱动技术相比，该方法在实现视觉上令人满意的修复方面表现出了显著的效果。

HySim: 图像修复中的快速混合相似度度量方法