# Feature Refinement to Improve High Resolution Image Inpainting

arXiv: https://arxiv.org/abs/2206.13644 · HTML: https://mlsanity.com/paper/2206.13644/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Large Scale Image Completion via Co-Modulated Generative Adversarial Networks](https://mlsanity.com/paper/2103.10428/) (2021)
- [MAT: Mask-Aware Transformer for Large Hole Image Inpainting](https://mlsanity.com/paper/2203.15270/) (2022)
- [Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://mlsanity.com/paper/2104.01431/) (2021)
- [Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding](https://mlsanity.com/paper/2203.00867/) (2022)
- [Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness](https://mlsanity.com/paper/2104.13743/) (2021)
- [Deep Component Analysis via Alternating Direction Neural Networks](https://mlsanity.com/paper/1803.06407/) (2018)

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

- [ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors](https://mlsanity.com/paper/2210.05950/) (2022)
- [Reconstructing Historical Climate Fields With Deep Learning](https://mlsanity.com/paper/2311.18348/) (2023)
- [MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models](https://mlsanity.com/paper/2302.13173/) (2023)
- [Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction](https://mlsanity.com/paper/2303.10344/) (2023)
- [Layout Aware Inpainting for Automated Furniture Removal in Indoor Scenes](https://mlsanity.com/paper/2210.15796/) (2022)
- [GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments](https://mlsanity.com/paper/2606.17520/) (2026)
- [RETHINED: A New Benchmark and Baseline for Real-Time High-Resolution Image Inpainting On Edge Devices](https://mlsanity.com/paper/2503.14757/) (2025)
- [REFINE-CONTROL: A Semi-supervised Distillation Method For Conditional Image Generation](https://mlsanity.com/paper/2509.22139/) (2025)

## Abstract (arXiv)

In this paper, we address the problem of degradation in inpainting quality of neural networks operating at high resolutions. Inpainting networks are often unable to generate globally coherent structures at resolutions higher than their training set. This is partially attributed to the receptive field remaining static, despite an increase in image resolution. Although downscaling the image prior to inpainting produces coherent structure, it inherently lacks detail present at higher resolutions. To get the best of both worlds, we optimize the intermediate featuremaps of a network by minimizing a multiscale consistency loss at inference. This runtime optimization improves the inpainting results and establishes a new state-of-the-art for high resolution inpainting. Code is available at: https://github.com/geomagical/lama-with-refiner/tree/refinement.