  Improving the aesthetic quality of images is challenging and eager for the
public. To address this problem, most existing algorithms are based on
supervised learning methods to learn an automatic photo enhancer for paired
data, which consists of low-quality photos and corresponding expert-retouched
versions. However, the style and characteristics of photos retouched by experts
may not meet the needs or preferences of general users. In this paper, we
present an unsupervised image enhancement generative adversarial network
(UEGAN), which learns the corresponding image-to-image mapping from a set of
images with desired characteristics in an unsupervised manner, rather than
learning on a large number of paired images. The proposed model is based on
single deep GAN which embeds the modulation and attention mechanisms to capture
richer global and local features. Based on the proposed model, we introduce two
losses to deal with the unsupervised image enhancement: (1) fidelity loss,
which is defined as a L2 regularization in the feature domain of a pre-trained
VGG network to ensure the content between the enhanced image and the input
image is the same, and (2) quality loss that is formulated as a relativistic
hinge adversarial loss to endow the input image the desired characteristics.
Both quantitative and qualitative results show that the proposed model
effectively improves the aesthetic quality of images. Our code is available at:
https://github.com/eezkni/UEGAN.
