# Deep Blind Image Inpainting

arXiv: https://arxiv.org/abs/1712.09078 · HTML: https://mlsanity.com/paper/1712.09078/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2017.

## Built on by (most cited first, 8 of 8)

- [RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection](https://mlsanity.com/paper/2302.12464/) (2023)
- [FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting](https://mlsanity.com/paper/2108.04424/) (2021)
- [BVINet: Unlocking Blind Video Inpainting with Zero Annotations](https://mlsanity.com/paper/2502.01181/) (2025)
- [Hierarchical Contrastive Learning for Pattern-Generalizable Image Corruption Detection](https://mlsanity.com/paper/2308.14061/) (2023)
- [RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework](https://mlsanity.com/paper/2202.05988/) (2022)
- [Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs](https://mlsanity.com/paper/2205.06597/) (2022)
- [Blind Inpainting with Object-aware Discrimination for Artificial Marker Removal](https://mlsanity.com/paper/2303.15124/) (2023)
- [Context-Aware Mutual Learning for Blind Image Inpainting and Beyond](https://mlsanity.com/paper/2609.14439/) (2026)

## Abstract (arXiv)

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing infor- mation in corrupted regions. However, directly using exist- ing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions. To solve this problem, we introduce an encoder and decoder architecture to capture more useful information and develop a robust loss function to deal with outliers. Our algorithm can predict the missing information in the corrupted regions, thus facilitating the clear image restoration. Both qualitative and quantitative experimental demonstrate that our algorithm can deal with the corrupted regions of arbitrary shapes and performs favorably against state-of-the-art methods.