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Image harmonization with Simple Hybrid CNN-Transformer Network.

Guanlin Li1, Bin Zhao2, Xuelong Li2

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, Shannxi, China; School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, 710072, Shannxi, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 11, 2024
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Summary
This summary is machine-generated.

This study introduces a new Simple Hybrid CNN-Transformer Network (SHT-Net) for image harmonization. SHT-Net effectively models global-local pixel illumination, enhancing image detail and color consistency.

Keywords:
Hybrid CNN-transformer structureImage harmonizationModulated convolutionParallel attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Image harmonization aims to match foreground illumination to background illumination in composite images.
  • Current methods struggle to capture essential global-local pixel illumination dependencies for sharp, color-consistent results.

Purpose of the Study:

  • To develop a novel network, SHT-Net, capable of establishing global-local pixel illumination dependencies for improved image harmonization.
  • To generate photo-realistic harmonized images with fine-grained details and consistent colors.

Main Methods:

  • Designed a symmetrical hierarchical architecture named Simple Hybrid CNN-Transformer Network (SHT-Net).
  • Incorporated two novel Transformer blocks: a scale-aware gated block for multi-scale feature capture and a parallel attention block for global-local illumination modeling.
  • Utilized an efficient feed-forward network to refine features for realistic output.

Main Results:

  • Achieved promising quantitative and qualitative results on standard image harmonization benchmarks.
  • Demonstrated the effectiveness of SHT-Net in capturing multi-scale features and modeling pixel illumination relationships.
  • Generated harmonized images with enhanced detail and color consistency.

Conclusions:

  • SHT-Net offers a significant advancement in image harmonization by effectively modeling global-local illumination dependencies.
  • The proposed architecture and attention mechanisms contribute to generating high-quality, photo-realistic harmonized images.
  • The method provides a robust solution for challenges in composite image illumination transfer.