  In this work, we aim to learn an unpaired image enhancement model, which can
enrich low-quality images with the characteristics of high-quality images
provided by users. We propose a quality attention generative adversarial
network (QAGAN) trained on unpaired data based on the bidirectional Generative
Adversarial Network (GAN) embedded with a quality attention module (QAM). The
key novelty of the proposed QAGAN lies in the injected QAM for the generator
such that it learns domain-relevant quality attention directly from the two
domains. More specifically, the proposed QAM allows the generator to
effectively select semantic-related characteristics from the spatial-wise and
adaptively incorporate style-related attributes from the channel-wise,
respectively. Therefore, in our proposed QAGAN, not only discriminators but
also the generator can directly access both domains which significantly
facilitates the generator to learn the mapping function. Extensive experimental
results show that, compared with the state-of-the-art methods based on unpaired
learning, our proposed method achieves better performance in both objective and
subjective evaluations.
