  Generative Adversarial Networks (GANs) have been widely used for the
image-to-image translation task. While these models rely heavily on the labeled
image pairs, recently some GAN variants have been proposed to tackle the
unpaired image translation task. These models exploited supervision at the
domain level with a reconstruction process for unpaired image translation. On
the other hand, parallel works have shown that leveraging perceptual loss
functions based on high level deep features could enhance the generated image
quality. Nevertheless, as these GAN-based models either depended on the
pretrained deep network structure or relied on the labeled image pairs, they
could not be directly applied to the unpaired image translation task. Moreover,
despite the improvement of the introduced perceptual losses from deep neural
networks, few researchers have explored the possibility of improving the
generated image quality from classical image quality measures. To tackle the
above two challenges, in this paper, we propose a unified quality-aware
GAN-based framework for unpaired image-to-image translation, where a
quality-aware loss is explicitly incorporated by comparing each source image
and the reconstructed image at the domain level. Specifically, we design two
detailed implementations of the quality loss. The first method is based on a
classical image quality assessment measure by defining a classical
quality-aware loss. The second method proposes an adaptive deep network based
loss. Finally, extensive experimental results on many real-world datasets
clearly show the quality improvement of our proposed framework, and the
superiority of leveraging classical image quality measures for unpaired image
translation compared to the deep network based model.
