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Illustration image style transfer method design based on improved cyclic consistent adversarial network.

Xiaojun Wang1, Jing Jiang1

  • 1College of Arts, Anhui Xinhua University, Hefei, China.

Plos One
|January 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient illustration style transfer method using attention mechanisms and cycle-consistent adversarial networks. The approach enhances realism while preserving artistic details, achieving high accuracy and superior image quality.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Style transfer aims to render images in the artistic style of another.
  • Existing methods struggle to balance style transformation with content preservation, especially for illustrations.

Purpose of the Study:

  • To develop an efficient style transfer method for illustrations.
  • To improve the expressiveness and realism of illustration images.
  • To maintain artistic integrity during style transfer.

Main Methods:

  • Combines attention mechanisms with cycle-consistent adversarial networks (CycleGAN).
  • Introduces an improved attention module to adaptively highlight key visual elements.
  • Utilizes capabilities of both image restoration and style transfer.

Main Results:

  • Achieved high-quality style transfer with high accuracy (>95%) and detail retention.
  • Demonstrated low loss function values (approaching 0.00) and minimal FID scores on benchmark datasets.
  • Resulting images exhibit more vibrant colors and distinct line features.

Conclusions:

  • The proposed method effectively transfers style to illustrations while preserving original features.
  • The approach offers superior accuracy and detail preservation compared to existing methods.
  • The technique shows practical applicability for enhancing illustration realism and expressiveness.