Towards Unsupervised Deep Image Enhancement With Generative Adversarial Network

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
$\ell 2$
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
.