# Progressive Image Inpainting with Full-Resolution Residual Network

arXiv: https://arxiv.org/abs/1907.10478 · HTML: https://mlsanity.com/paper/1907.10478/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 19 of 19)

- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [MAT: Mask-Aware Transformer for Large Hole Image Inpainting](https://mlsanity.com/paper/2203.15270/) (2022)
- [Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2304.03322/) (2023)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Image Inpainting via Tractable Steering of Diffusion Models](https://mlsanity.com/paper/2401.03349/) (2023)
- [FRIH: Fine-grained Region-aware Image Harmonization](https://mlsanity.com/paper/2205.06448/) (2022)
- [Image Inpainting via Iteratively Decoupled Probabilistic Modeling](https://mlsanity.com/paper/2212.02963/) (2022)
- [View-consistent Object Removal in Radiance Fields](https://mlsanity.com/paper/2408.02100/) (2024)
- [ReGO: Reference-Guided Outpainting for Scenery Image](https://mlsanity.com/paper/2106.10601/) (2021)
- [Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning](https://mlsanity.com/paper/2303.13133/) (2023)
- [Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting](https://mlsanity.com/paper/2403.18186/) (2024)
- [GRIG: Few-Shot Generative Residual Image Inpainting](https://mlsanity.com/paper/2304.12035/) (2023)
- [PRISM: Progressive Restoration for Scene Graph-based Image Manipulation](https://mlsanity.com/paper/2311.02247/) (2023)
- [An Analysis of Generative Methods for Multiple Image Inpainting](https://mlsanity.com/paper/2205.02146/) (2022)
- [E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space](https://mlsanity.com/paper/2403.12197/) (2024)
- [Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels](https://mlsanity.com/paper/2112.09262/) (2021)
- [Iterative Geometry-Aware Cross Guidance Network for Stereo Image Inpainting](https://mlsanity.com/paper/2205.03825/) (2022)
- [Progressive with Purpose: Guiding Progressive Inpainting DNNs through Context and Structure](https://mlsanity.com/paper/2209.10071/) (2022)
- [Multi-stage Progressive Reasoning for Dunhuang Murals Inpainting](https://mlsanity.com/paper/2305.05902/) (2023)

## Abstract (arXiv)

Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes. However, they fail to generate plausible textures inside damaged area because there lacks surrounding information. A progressive inpainting approach would be advantageous for eliminating central blurriness, i.e., restoring well and then updating masks. In this paper, we propose full-resolution residual network (FRRN) to fill irregular holes, which is proved to be effective for progressive image inpainting. We show that well-designed residual architecture facilitates feature integration and texture prediction. Additionally, to guarantee completion quality during progressive inpainting, we adopt N Blocks, One Dilation strategy, which assigns several residual blocks for one dilation step. Correspondingly, a step loss function is applied to improve the performance of intermediate restorations. The experimental results demonstrate that the proposed FRRN framework for image inpainting is much better than previous methods both quantitatively and qualitatively. Our codes are released at: urlhttps://github.com/ZongyuGuo/InpaintingFRRN.