  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.
