# Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels

arXiv: https://arxiv.org/abs/2112.09262 · HTML: https://mlsanity.com/paper/2112.09262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning](https://mlsanity.com/paper/1901.00212/) (2019)
- [Progressive Image Inpainting with Full-Resolution Residual Network](https://mlsanity.com/paper/1907.10478/) (2019)
- [Structural inpainting](https://mlsanity.com/paper/1803.10348/) (2018)
- [Image Inpainting by Adaptive Fusion of Variable Spline Interpolations](https://mlsanity.com/paper/1911.00825/) (2019)
- [Image Inpainting by Multiscale Spline Interpolation](https://mlsanity.com/paper/2001.03270/) (2020)

## Abstract (arXiv)

Image inpainting is an effective method to enhance distorted digital images. Different inpainting methods use the information of neighboring pixels to predict the value of missing pixels. Recently deep neural networks have been used to learn structural and semantic details of images for inpainting purposes. In this paper, we propose a network for image inpainting. This network, similar to U-Net, extracts various features from images, leading to better results. We improved the final results by replacing the damaged pixels with the recovered pixels of the output images. Our experimental results show that this method produces high-quality results compare to the traditional methods.