# Image inpainting using frequency domain priors

arXiv: https://arxiv.org/abs/2012.01832 · HTML: https://mlsanity.com/paper/2012.01832/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection](https://mlsanity.com/paper/2409.09953/) (2024)
- [Painterly Image Harmonization in Dual Domains](https://mlsanity.com/paper/2212.08846/) (2022)
- [Both Spatial and Frequency Cues Contribute to High-Fidelity Image Inpainting](https://mlsanity.com/paper/2307.07678/) (2023)
- [Time-variant Image Inpainting via Interactive Distribution Transition Estimation](https://mlsanity.com/paper/2506.23461/) (2025)

## Abstract (arXiv)

In this paper, we present a novel image inpainting technique using frequency domain information. Prior works on image inpainting predict the missing pixels by training neural networks using only the spatial domain information. However, these methods still struggle to reconstruct high-frequency details for real complex scenes, leading to a discrepancy in color, boundary artifacts, distorted patterns, and blurry textures. To alleviate these problems, we investigate if it is possible to obtain better performance by training the networks using frequency domain information (Discrete Fourier Transform) along with the spatial domain information. To this end, we propose a frequency-based deconvolution module that enables the network to learn the global context while selectively reconstructing the high-frequency components. We evaluate our proposed method on the publicly available datasets CelebA, Paris Streetview, and DTD texture dataset, and show that our method outperforms current state-of-the-art image inpainting techniques both qualitatively and quantitatively.