  This paper tackles unpaired image enhancement, a task of learning a mapping
function which transforms input images into enhanced images in the absence of
input-output image pairs. Our method is based on generative adversarial
networks (GANs), but instead of simply generating images with a neural network,
we enhance images utilizing image editing software such as Adobe Photoshop for
the following three benefits: enhanced images have no artifacts, the same
enhancement can be applied to larger images, and the enhancement is
interpretable. To incorporate image editing software into a GAN, we propose a
reinforcement learning framework where the generator works as the agent that
selects the software's parameters and is rewarded when it fools the
discriminator. Our framework can use high-quality non-differentiable filters
present in image editing software, which enables image enhancement with high
performance. We apply the proposed method to two unpaired image enhancement
tasks: photo enhancement and face beautification. Our experimental results
demonstrate that the proposed method achieves better performance, compared to
the performances of the state-of-the-art methods based on unpaired learning.
