# Semi-parametric Image Inpainting

arXiv: https://arxiv.org/abs/1807.02855 · HTML: https://mlsanity.com/paper/1807.02855/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [Foreground-guided Facial Inpainting with Fidelity Preservation](https://mlsanity.com/paper/2105.03342/) (2021)
- [V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network](https://mlsanity.com/paper/2201.00323/) (2022)
- [Multi-Modality Image Inpainting using Generative Adversarial Networks](https://mlsanity.com/paper/2206.09210/) (2022)
- [WSSL: Weighted Self-supervised Learning Framework For Image-inpainting](https://mlsanity.com/paper/2211.13856/) (2022)
- [Reference Guided Image Inpainting using Facial Attributes](https://mlsanity.com/paper/2301.08044/) (2023)
- [DIFAI: Diverse Facial Inpainting using StyleGAN Inversion](https://mlsanity.com/paper/2301.08443/) (2023)

## Abstract (arXiv)

This paper introduces a semi-parametric approach to image inpainting for irregular holes. The nonparametric part consists of an external image database. During test time database is used to retrieve a supplementary image, similar to the input masked picture, and utilize it as auxiliary information for the deep neural network. Further, we propose a novel method of generating masks with irregular holes and present public dataset with such masks. Experiments on CelebA-HQ dataset show that our semi-parametric method yields more realistic results than previous approaches, which is confirmed by the user study.