Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e.g., 4x and 8x). The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Dense Block (RIDB). Moreover, we propose a joint discriminator which discriminates both image data and embedded semantics. The joint discriminator learns the joint probability distribution of the image space and latent space. We also use a Relativistic average Least Squares loss (RaLS) as the adversarial loss to alleviate the gradient vanishing problem and enhance the stability of the training procedure. Extensive experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons.
翻译:超分辨率是一个针对域的超分辨率图像超分辨率,目的是从低分辨率(LR)对应方生成高分辨率(HR)脸部图像。在本文中,我们提议了一种新型面部超分辨率方法,即Semantic Encoder 指导的合成合成反反面超分辨率网络(SEGA-FURN),以超升因子(例如,4x和8x)将不匹配的微小LR脸部图像解析到其HR对应方,以多重超升因子(例如,4x和8x)为主。拟议网络由新颖的语义解码组成,能够捕捉嵌入的语义学导对抗性学习的新式超分辨率生成器,以及使用名为 " 内部音频区 " (RIDB) 后端结构的新型生成器。此外,我们提议了一种区分图像数据和嵌入语系语系超嵌定语系的聚合体图像空间和潜伏空间的概率分布。我们还使用一种相对性平均最小方位损失(RALS)作为对抗性最小方位损失,以缓解加速消减问题,并增强培训程序稳定性。我们提议的超大型数据变制方法在高度测试中可以实现高分辨率测试。