# Image Harmonization by Matching Regional References

arXiv: https://arxiv.org/abs/2204.04715 · HTML: https://mlsanity.com/paper/2204.04715/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [DoveNet: Deep Image Harmonization via Domain Verification](https://mlsanity.com/paper/1911.13239/) (2019)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- [Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations](https://mlsanity.com/paper/2007.06929/) (2020)
- [Improving the Harmony of the Composite Image by Spatial-Separated Attention Module](https://mlsanity.com/paper/1907.06406/) (2019)
- [Region-aware Adaptive Instance Normalization for Image Harmonization](https://mlsanity.com/paper/2106.02853/) (2021)
- [Foreground-aware Semantic Representations for Image Harmonization](https://mlsanity.com/paper/2006.00809/) (2020)
- [BargainNet: Background-Guided Domain Translation for Image Harmonization](https://mlsanity.com/paper/2009.09169/) (2020)
- [Rethinking and Improving the Robustness of Image Style Transfer](https://mlsanity.com/paper/2104.05623/) (2021)
- [Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer](https://mlsanity.com/paper/2104.05376/) (2021)
- [Style-Aware Normalized Loss for Improving Arbitrary Style Transfer](https://mlsanity.com/paper/2104.10064/) (2021)

## Built on by (most cited first, 6 of 6)

- [Deep Image Harmonization in Dual Color Spaces](https://mlsanity.com/paper/2308.02813/) (2023)
- [Deep Image Harmonization with Globally Guided Feature Transformation and Relation Distillation](https://mlsanity.com/paper/2308.00356/) (2023)
- [Painterly Image Harmonization by Learning from Painterly Objects](https://mlsanity.com/paper/2312.10263/) (2023)
- [Progressive Painterly Image Harmonization from Low-level Styles to High-level Styles](https://mlsanity.com/paper/2312.10264/) (2023)
- [ConsiStyle: Style Diversity in Training-Free Consistent T2I Generation](https://mlsanity.com/paper/2505.20626/) (2025)
- [Retrieval Augmented Image Harmonization](https://mlsanity.com/paper/2412.13916/) (2024)

## Abstract (arXiv)

To achieve visual consistency in composite images, recent image harmonization methods typically summarize the appearance pattern of global background and apply it to the global foreground without location discrepancy. However, for a real image, the appearances (illumination, color temperature, saturation, hue, texture, etc) of different regions can vary significantly. So previous methods, which transfer the appearance globally, are not optimal. Trying to solve this issue, we firstly match the contents between the foreground and background and then adaptively adjust every foreground location according to the appearance of its content-related background regions. Further, we design a residual reconstruction strategy, that uses the predicted residual to adjust the appearance, and the composite foreground to reserve the image details. Extensive experiments demonstrate the effectiveness of our method. The source code will be available publicly.