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Guided Cyclegan Via Semi-Dual Optimal Transport for Photo-Realistic Face Super-Resolution

机译：引导的Cyclegan通过半双最优传输实现逼真的人脸超分辨率

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Face super-resolution has been studied for decades, and many approaches have been proposed to upsample low-resolution face images using information mined from paired low-resolution (LR) images and high-resolution (HR) images. However, most of this kind of works only simply sharpen the blurry edges in the upsampled face images and typically no photo-realistic face is reconstructed in the final result. In this paper, we present a GAN-based algorithm for face super-resolution which properly synthesizes photo-realistic super-recovered face. To this end, we introduce semi-dual optimal transport to optimize our model such that the distribution of its generated data can match the distribution of a target domain as much as possible. This way would endow our model with learning the mapping of distribution from unpaired LR images and HR images with desired properties. We demonstrate the robustness of our algorithm by testing it on Color FERET database and show that its performance is considerably superior to all state-of-the-art approaches.
机译：面部超分辨率已经研究了数十年，并且已经提出了许多方法，这些方法使用从成对的低分辨率（LR）图像和高分辨率（HR）图像中提取的信息来对低分辨率的面部图像进行升采样。但是，大多数此类工作仅简单地锐化了上采样面部图像中的模糊边缘，并且通常不会在最终结果中重建照片般逼真的面部。在本文中，我们提出了一种基于GAN的人脸超分辨率算法，该算法可以正确合成照片般逼真的超恢复人脸。为此，我们引入了半对偶最优传输来优化我们的模型，以便其生成的数据的分布可以尽可能匹配目标域的分布。这样，我们的模型就可以从具有所需属性的未配对LR图像和HR图像中学习分布的映射。通过在Color FERET数据库上进行测试，我们证明了该算法的鲁棒性，并表明其性能大大优于所有最新方法。

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