Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software
This addresses the problem of producing high-quality, artifact-free image enhancements for applications like photography and beautification, though it is incremental as it builds on existing GAN and reinforcement learning techniques.
The paper tackles unpaired image enhancement by learning a mapping function without input-output pairs, using a reinforcement learning framework to control image editing software like Adobe Photoshop, and achieves better performance than state-of-the-art methods in tasks such as photo enhancement and face beautification.
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.