HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting
This work addresses the need for efficient model-driven inpainting in data-constrained applications like medical imaging and remote sensing, though it appears incremental as it builds on existing patch-based methods.
The paper tackled the problem of patch matching in image inpainting by proposing HySim, a hybrid similarity measure combining Chebychev and Minkowski distances, which reduced mismatch errors and achieved high-quality inpainting results, as demonstrated experimentally against other model-driven techniques.
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.