Abstract
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.
Abstract (translated)
修复缺失图像区域是各种应用中的一项关键任务，如医学成像和遥感。趋势数据驱动的方法在图像修复方面效率很高，但通常需要进行大量的数据预处理。在应用受限数据可用性和质量的情况下，尤其是在与时间序列预测相关的应用中，模型驱动方法仍然有必要。本文提出了一种基于补丁技术的改进模型驱动方法。我们的方法与标准的平方差相似度（SSD）相似度度量方法有所区别，通过引入混合相似性（HySim），结合了切比雪夫距离和Minkowski距离的优势。这种杂糅增强了补丁选择，导致修复结果质量高，匹配误差降低。实验结果证明，我们的方法对其他模型驱动方法（如扩散或基于补丁的方法）的有效性进行了展示，突出了在实现观感良好的修复效果方面的有效性。
URL
https://arxiv.org/abs/2403.14292
