  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.
