# Image Fine-grained Inpainting

arXiv: https://arxiv.org/abs/2002.02609 · HTML: https://mlsanity.com/paper/2002.02609/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion](https://mlsanity.com/paper/2403.18818/) (2024)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Magic Insert: Style-Aware Drag-and-Drop](https://mlsanity.com/paper/2407.02489/) (2024)
- [SketchFFusion: Sketch-guided image editing with diffusion model](https://mlsanity.com/paper/2304.03174/) (2023)
- [RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning](https://mlsanity.com/paper/2209.08630/) (2022)
- [Image Inpainting Using Wasserstein Generative Adversarial Imputation Network](https://mlsanity.com/paper/2106.15341/) (2021)
- [Computer Vision Based Parking Optimization System](https://mlsanity.com/paper/2201.00095/) (2022)
- [PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting](https://mlsanity.com/paper/2304.06107/) (2023)
- [SFI-Swin: Symmetric Face Inpainting with Swin Transformer by Distinctly Learning Face Components Distributions](https://mlsanity.com/paper/2301.03130/) (2023)
- [Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements](https://mlsanity.com/paper/2504.19197/) (2025)
- [Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion](https://mlsanity.com/paper/2504.13791/) (2025)
- [Towards Seamless Borders: A Method for Mitigating Inconsistencies in Image Inpainting and Outpainting](https://mlsanity.com/paper/2506.12530/) (2025)
- [Both Spatial and Frequency Cues Contribute to High-Fidelity Image Inpainting](https://mlsanity.com/paper/2307.07678/) (2023)
- [Direct May Not Be the Best: An Incremental Evolution View of Pose Generation](https://mlsanity.com/paper/2404.08419/) (2024)

## Abstract (arXiv)

Image inpainting techniques have shown promising improvement with the assistance of generative adversarial networks (GANs) recently. However, most of them often suffered from completed results with unreasonable structure or blurriness. To mitigate this problem, in this paper, we present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. Benefited from the property of this network, we can more easily recover large regions in an incomplete image. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss for concentrating on uncertain areas and enhancing the semantic details. Besides, we devise a geometrical alignment constraint item to compensate for the pixel-based distance between prediction features and ground-truth ones. We also employ a discriminator with local and global branches to ensure local-global contents consistency. To further improve the quality of generated images, discriminator feature matching on the local branch is introduced, which dynamically minimizes the similarity of intermediate features between synthetic and ground-truth patches. Extensive experiments on several public datasets demonstrate that our approach outperforms current state-of-the-art methods. Code is available at https://github.com/Zheng222/DMFN.