# HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting

arXiv: https://arxiv.org/abs/2403.14292 · HTML: https://mlsanity.com/paper/2403.14292/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Deep Semantic Face Deblurring](https://mlsanity.com/paper/1803.03345/) (2018)
- [VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal](https://mlsanity.com/paper/1904.06726/) (2019)
- [Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier](https://mlsanity.com/paper/2112.12549/) (2021)
- [Unsupervised Learnable Sinogram Inpainting Network (SIN) for Limited Angle CT reconstruction](https://mlsanity.com/paper/1811.03911/) (2018)
- [Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors](https://mlsanity.com/paper/1805.04239/) (2018)
- [Predicting the Future is like Completing a Painting!](https://mlsanity.com/paper/2011.04750/) (2020)
- [A nonlocal feature-driven exemplar-based approach for image inpainting](https://mlsanity.com/paper/1909.09301/) (2019)

## Built on by (most cited first, 1 of 1)

- [Hinge-FM2I: An Approach using Image Inpainting for Interpolating Missing Data in Univariate Time Series](https://mlsanity.com/paper/2406.06631/) (2024)

## Abstract (arXiv)

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.