Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution (HR) face images, is a domain-specific image super-resolution problem. Recently, FSR has received considerable attention and witnessed dazzling advances with the development of deep learning techniques. To date, few summaries of the studies on the deep learning-based FSR are available. In this survey, we present a comprehensive review of deep learning-based FSR methods in a systematic manner. First, we summarize the problem formulation of FSR and introduce popular assessment metrics and loss functions. Second, we elaborate on the facial characteristics and popular datasets used in FSR. Third, we roughly categorize existing methods according to the utilization of facial characteristics. In each category, we start with a general description of design principles, then present an overview of representative approaches, and then discuss the pros and cons among them. Fourth, we evaluate the performance of some state-of-the-art methods. Fifth, joint FSR and other tasks, and FSR-related applications are roughly introduced. Finally, we envision the prospects of further technological advancement in this field. A curated list of papers and resources to face super-resolution are available at \url{https://github.com/junjun-jiang/Face-Hallucination-Benchmark}
翻译:面部超分辨率(FSR)也称为面部幻觉,其目的是加强低分辨率(LR)脸部图像的解决方案,以生成高分辨率(HR)脸部图像,这是一个针对特定领域的图像超分辨率问题。最近,FSR受到相当多的关注,在深层学习技术的开发方面目睹了惊人的进展。到目前为止,关于深层学习基础FSR的研究的总结很少。在这次调查中,我们以系统的方式对深层次学习基础FSR方法进行全面审查。首先,我们总结了FSR的问题,并引入了流行评估度量度和损失功能。第二,我们详细介绍了FSR所使用的面部特征和流行数据集。第三,我们根据面部特征的利用情况对现有方法进行了大致分类。在每一类中,我们先对设计原则作一般性描述,然后概述代表性方法,然后讨论其中的利弊。第四,我们评估一些以艺术为基础的方法的绩效。第五,联合FSR和其他任务,以及与FSR有关的应用。我们大致介绍了FSR-mart。第三,我们从使用面面面图中设想了现有方法的前景。