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Reference-aware image harmonization.

Han Guo1, Hongling Gu1, Bolun Zheng1

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.

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|December 12, 2025
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Summary
This summary is machine-generated.

This study introduces Reference-Aware Image Harmonization Network (RANet) to improve image harmonization by using reference modules for global style and local region alignment, even without similar content.

Keywords:
Appearance translationDiffusion modelImage editingImage harmonization

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Image harmonization is vital for realistic image composition.
  • It's an ill-posed problem due to a lack of clear adjustment standards.
  • Existing methods struggle when foreground and background lack similar content.

Purpose of the Study:

  • To develop a novel network for robust image harmonization.
  • To address limitations of current methods in scenarios with dissimilar foreground-background content.
  • To improve both global style and local visual consistency in harmonized images.

Main Methods:

  • Proposed Reference-Aware Image Harmonization Network (RANet).
  • Introduced a Style-aware Reference Module using a diffusion model for global style alignment.
  • Designed a Region-aware Reference Module with soft masks for adaptive local region utilization.

Main Results:

  • RANet achieves effective image harmonization.
  • Demonstrated improved performance on the iHarmony4 dataset and a real-world mobile dataset.
  • Validated the effectiveness of both global style and local region awareness.

Conclusions:

  • The proposed RANet effectively harmonizes images, even without similar foreground-background content.
  • The Style-aware and Region-aware modules enhance harmonization quality.
  • The method offers a significant advancement in image composition and editing.