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.
翻译:本文处理未受重视的图像增强, 这是一项在没有输入输出图像配对的情况下学习将输入图像转换成增强图像的绘图功能的任务。 我们的方法基于基因对抗网络( GANs ), 我们的方法不是简单地用神经网络生成图像, 而是利用图像编辑软件( 如 Adobe Photoshop ) 来提升图像, 有三个好处: 强化图像没有工艺品, 同样的增强功能可以应用到更大的图像, 增强功能是可以解释的 。 为了将图像编辑软件纳入 GAN, 我们提议了一个强化学习框架, 让生成者作为代理来选择软件参数, 当它愚弄歧视者时得到奖励 。 我们的框架可以使用图像编辑软件中显示的高质量、 无差别的过滤器, 从而能够高性能地提升图像。 我们将拟议的方法应用于两个未受重视的图像增强任务: 照片增强和面部美化。 我们的实验结果表明, 与基于未受重视的学习的状态方法的性能相比, 。