# Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention

arXiv: https://arxiv.org/abs/2009.01031 · HTML: https://mlsanity.com/paper/2009.01031/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 15 of 15)

- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Pixel-Inconsistency Modeling for Image Manipulation Localization](https://mlsanity.com/paper/2310.00234/) (2023)
- [HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention](https://mlsanity.com/paper/2402.14185/) (2024)
- [Balanced Classification: A Unified Framework for Long-Tailed Object Detection](https://mlsanity.com/paper/2308.02213/) (2023)
- [Towards Interactive Image Inpainting via Sketch Refinement](https://mlsanity.com/paper/2306.00407/) (2023)
- [ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification](https://mlsanity.com/paper/2409.13349/) (2024)
- [Learning Position-Aware Implicit Neural Network for Real-World Face Inpainting](https://mlsanity.com/paper/2401.10537/) (2024)
- [Dense Feature Interaction Network for Image Inpainting Localization](https://mlsanity.com/paper/2408.02191/) (2024)
- [InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models](https://mlsanity.com/paper/2501.02816/) (2025)
- [LookupForensics: A Large-Scale Multi-Task Dataset for Multi-Phase Image-Based Fact Verification](https://mlsanity.com/paper/2407.18614/) (2024)
- [GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention](https://mlsanity.com/paper/2101.07419/) (2021)
- [Wavelet Prior Attention Learning in Axial Inpainting Network](https://mlsanity.com/paper/2206.03113/) (2022)
- [Both Spatial and Frequency Cues Contribute to High-Fidelity Image Inpainting](https://mlsanity.com/paper/2307.07678/) (2023)
- [ESDiff: Encoding Strategy-inspired Diffusion Model with Few-shot Learning for Color Image Inpainting](https://mlsanity.com/paper/2504.17524/) (2025)
- [Time-variant Image Inpainting via Interactive Distribution Transition Estimation](https://mlsanity.com/paper/2506.23461/) (2025)

## Abstract (arXiv)

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting. The DL-based image inpainting approaches can produce visually plausible results, but often generate various unpleasant artifacts, especially in the boundary and highly textured regions. To tackle this challenge, in this work, we propose a new end-to-end, two-stage (coarse-to-fine) generative model through combining a local binary pattern (LBP) learning network with an actual inpainting network. Specifically, the first LBP learning network using U-Net architecture is designed to accurately predict the structural information of the missing region, which subsequently guides the second image inpainting network for better filling the missing pixels. Furthermore, an improved spatial attention mechanism is integrated in the image inpainting network, by considering the consistency not only between the known region with the generated one, but also within the generated region itself. Extensive experiments on public datasets including CelebA-HQ, Places and Paris StreetView demonstrate that our model generates better inpainting results than the state-of-the-art competing algorithms, both quantitatively and qualitatively. The source code and trained models will be made available at https://github.com/HighwayWu/ImageInpainting.