论文标题
具有弹性示例的图像增强功能，并具有任务自适应全局功能自引导网络
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network
论文作者
论文摘要
我们提出了第一个实用的多任务图像增强网络，该网络能够学习一对多且多一的图像映射。我们表明，我们的模型在学习单个增强映射方面优于当前的艺术状态，而参数的参数明显少于竞争对手。此外，该模型通过利用共享表示形式同时学习多个映射的性能甚至更高的性能。我们的网络基于最近提出的SGN体系结构，其修改针对纳入全球功能和样式适应。最后，我们提出了一种基于生成的对抗网络（GAN）的多任务图像增强的未配对学习方法。
We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings. We show that our model outperforms the current state of the art in learning a single enhancement mapping, while having significantly fewer parameters than its competitors. Furthermore, the model achieves even higher performance on learning multiple mappings simultaneously, by taking advantage of shared representations. Our network is based on the recently proposed SGN architecture, with modifications targeted at incorporating global features and style adaption. Finally, we present an unpaired learning method for multitask image enhancement, that is based on generative adversarial networks (GANs).
