Image Harmonization by Matching Regional References
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 10 Apr 2022.
Builds on
16 of 27 references.
References · 16
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
- 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,187
- 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
- 2017Universal Style Transfer via Feature Transformscited by 352
- 2018Arbitrary Style Transfer with Style-Attentional Networkscited by 197
- 2019DoveNet: Deep Image Harmonization via Domain Verificationcited by 134
- 2017Deep Image Harmonizationcited by 120
- 2020Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizationscited by 102
- 2019Improving the Harmony of the Composite Image by Spatial-Separated Attention Modulecited by 73
- 2021Region-aware Adaptive Instance Normalization for Image Harmonizationcited by 71
- 2020Foreground-aware Semantic Representations for Image Harmonizationcited by 53
4 more
- 2020BargainNet: Background-Guided Domain Translation for Image Harmonizationcited by 52
- 2021Rethinking and Improving the Robustness of Image Style Transfercited by 48
- 2021Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfercited by 41
- 2021Style-Aware Normalized Loss for Improving Arbitrary Style Transfercited by 30
Built on by
Citing papers per year: 2023: 4; 2024: 1; 2025: 1.
6 papers.
- 2023Deep Image Harmonization in Dual Color Spacescited by 12
- 2023Deep Image Harmonization with Globally Guided Feature Transformation and Relation Distillationcited by 4
- 2023Painterly Image Harmonization by Learning from Painterly Objectscited by 3
- 2023Progressive Painterly Image Harmonization from Low-level Styles to High-level Stylescited by 1
- 2025ConsiStyle: Style Diversity in Training-Free Consistent T2I Generationcited by 1
- 2024Retrieval Augmented Image Harmonizationcited by 0
- 2025ConsiStyle: Style Diversity in Training-Free Consistent T2I Generation27 May 2025
- 2024Retrieval Augmented Image Harmonization18 Dec 2024
- 2023Progressive Painterly Image Harmonization from Low-level Styles to High-level Styles15 Dec 2023
- 2023Painterly Image Harmonization by Learning from Painterly Objects15 Dec 2023
- 2023Deep Image Harmonization in Dual Color Spaces5 Aug 2023
- 2023Deep Image Harmonization with Globally Guided Feature Transformation and Relation Distillation1 Aug 2023