# A nonlocal feature-driven exemplar-based approach for image inpainting

arXiv: https://arxiv.org/abs/1909.09301 · HTML: https://mlsanity.com/paper/1909.09301/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2308.05264/) (2023)
- [HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting](https://mlsanity.com/paper/2403.14292/) (2024)

## Abstract (arXiv)

We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geometric structures is achieved by using general convolution operators as a measure of behavior within an image. These are combined with a nonlocal exemplar-based approach to exploit the self-similarity of an image in the selected feature domains and to ensure the inpainting of textures. We also introduce an anisotropic patch distance metric to allow for better control of the feature selection within an image and present a nonlocal energy functional based on this metric. Finally, we derive an optimization algorithm for the proposed variational model and examine its validity experimentally with various test images.