Artifact-suppressed style transfer for Chinese ink paintings via enhanced CycleGAN

Key Points

• Artifact suppression significantly improves the quality of style transfer images, especially for fine details.
• The enhanced CycleGAN model effectively learns the artistic styles of Chinese ink paintings, showing notable fidelity to source styles.
• Employing neural networks allows for a more robust method of transferring style while minimizing artifacts present in original images.
• Future applications may enable higher quality image enhancement in various artistic domains, highlighting the model's versatility.