基于单次样本的超分辨跨域人脸缩小
Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar
摘要：
传统的人脸超分辨率方法通常假设测试低分辨率图像与训练图像位于同一域。由于光照条件和成像硬件的不同，在许多真实场景中，训练图像和测试图像之间不可避免地会出现域间隙。忽略这些域间隙将导致较差的面部超分辨率（FSR）性能。然而，如何将训练好的FSR模型有效地转移到目标域还没有研究。为了解决这个问题，我们开发了一个基于域感知金字塔的人脸超分辨率网络，命名为DAP-FSR网络。我们的DAP-FSR是第一次尝试超分辨率LR面对从目标域只利用一对高分辨率（HR）和LR样本在目标域。具体来说，我们的DAP-FSR首先使用其编码器来提取输入LR人脸的多尺度潜在表示。考虑到只有一个目标域的例子是可用的，我们建议增加目标域的数据混合的潜在表示的目标域的脸和源域的，然后馈送到我们的DAP-FSR的解码器的混合表示。解码器将生成类似于目标域图像风格的新面部图像。生成的HR面反过来用于优化我们的解码器，以减少域间隙。通过迭代更新潜在表示和我们的解码器，我们的DAP-FSR将适应目标域，从而实现真实和高质量的上采样HR人脸。在三个新构建的基准上进行的大量实验验证了我们的DAP-FSR的有效性和优越的性能相比，国家的最先进的。
英文摘要：
Conventional face super-resolution methods usually assume testing low-resolution (LR) images lie in the same domain as the training ones. Due to different lighting conditions and imaging hardware, domain gaps between training and testing images inevitably occur in many real-world scenarios. Neglecting those domain gaps would lead to inferior face super-resolution (FSR) performance. However, how to transfer a trained FSR model to a target domain efficiently and effectively has not been investigated. To tackle this problem, we develop a Domain-Aware Pyramid-based Face Super-Resolution network, named DAP-FSR network. Our DAP-FSR is the first attempt to super-resolve LR faces from a target domain by exploiting only a pair of high-resolution (HR) and LR exemplar in the target domain. To be specific, our DAP-FSR firstly employs its encoder to extract the multi-scale latent representations of the input LR face. Considering only one target domain example is available, we propose to augment the target domain data by mixing the latent representations of the target domain face and source domain ones, and then feed the mixed representations to the decoder of our DAP-FSR. The decoder will generate new face images resembling the target domain image style. The generated HR faces in turn are used to optimize our decoder to reduce the domain gap. By iteratively updating the latent representations and our decoder, our DAP-FSR will be adapted to the target domain, thus achieving authentic and high-quality upsampled HR faces. Extensive experiments on three newly constructed benchmarks validate the effectiveness and superior performance of our DAP-FSR compared to the state-of-the-art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
