HySim：一种用于图像修复中块匹配的高效混合相似度度量
HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting
AI总结：
本文提出一种用于图像修复块匹配的混合相似度度量HySim，结合切比雪夫和闵可夫斯基距离优势替代标准SSD，有效减少匹配错误并提升修复质量。
摘要：
图像修复用于填充缺失的图像区域，是医学成像和遥感等多种应用中的一项关键任务。为了提高图像修复中数据驱动方法的效率，通常需要大量的数据预处理。因此，在数据可用性和质量受限的应用场景下，特别是对于使用图像修复技术进行时间序列预测的相关应用，仍然需要基于模型的方法。本文提出了一种基于块技术的改进型模型驱动方法。该方法偏离了标准的平方差和（SSD）相似度度量，引入了混合相似度（HySim），结合了切比雪夫距离和闵可夫斯基距离的优势。这种混合方式增强了块选择，从而带来高质量的修复结果并减少了匹配错误。实验结果证明了该方法相对于其他模型驱动技术（如扩散或基于块的方法）的有效性，展示了其在实现视觉上令人愉悦的恢复方面的效果。
英文摘要：
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.
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