SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Generation
<|reference_start|>SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Generation: This paper handles the problem of converting real pictures into traditional Chinese ink-wash paintings, i.e., Chinese ink-wash painting style transfer. Though this problem could be realized by a wide range of image-to-image translation models, a notable issue with all these methods is that the original image content details could be easily erased or corrupted due to transfer of ink-wash style elements. To solve or ameliorate this issue, we propose to incorporate saliency detection into the unpaired image-to-image translation framework to regularize content information of the generated paintings. The saliency map is utilized for content regularization from two aspects, both explicitly and implicitly: (\romannumeral1) we propose saliency IOU (SIOU) loss to explicitly regularize saliency consistency before and after stylization; (\romannumeral2) we propose saliency adaptive normalization (SANorm) which implicitly enhances content integrity of the generated paintings by injecting saliency information to the generator network to guide painting generation. Besides, we also propose saliency attended discriminator network which harnesses saliency mask to focus generative adversarial attention onto salient image regions, it contributes to producing finer ink-wash stylization effect for salient objects of images. Qualitative and quantitative experiments consistently demonstrate superiority of our model over related advanced methods for Chinese ink-wash painting style transfer.<|reference_end|>
@article{gao2024sragan:,
    title={SRAGAN: Saliency Regularized and Attended Generative Adversarial Network
  for Chinese Ink-wash Painting Generation},
    author={Xiang Gao, Yuqi Zhang},
    journal={arXiv preprint arXiv:2404.15743},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15743},
    primaryClass={cs.CV}
}