Face sketch synthesis aims to generate a face sketch image from a corresponding photo image and has wide applications in law enforcement and digital entertainment. Despite the remarkable achievements that have been made in face sketch synthesis, most existing works pay main attention to the facial content transfer, at the expense of facial detail information. In this paper, we present a new generative adversarial learning framework to focus on detail preservation for realistic face sketch synthesis. Specifically, the high-resolution network is modified as generator to transform a face image from photograph to sketch domain. Except for the common adversarial loss, we design a detail loss to force the synthesized face sketch images have proximate details to its corresponding photo images. In addition, the style loss is adopted to restrain the synthesized face sketch images have vivid sketch style as the hand-drawn sketch images. Experimental results demonstrate that the proposed approach achieves superior performance, compared to state-of-the-art approaches, both on visual perception and objective evaluation. Specifically, this study indicated the higher FSIM values (0.7345 and 0.7080) and Scoot values (0.5317 and 0.5091) than most comparison methods on the CUFS and CUFSF datasets, respectively.

面部素描合成旨在从相应的照片图像生成面部素描图像，并且在执法和数字娱乐中具有广泛的应用。尽管在面部素描合成中取得了令人瞩目的成就，但大多数现有作品主要关注面部内容的传递，却以面部细节信息为代价。在本文中，我们提出了一种新的生成对抗性学习框架，该框架专注于细节保存以实现逼真的面部素描合成。具体而言，将高分辨率网络修改为生成器，以将面部图像从照片转换为草图域。除了常见的对抗损失外，我们还设计了一个细节损失，以迫使合成的面部素描图像与其相应的照片图像具有最接近的细节。此外，采用风格损失来抑制合成人脸素描图像具有逼真的素描风格。实验结果表明，与最新技术相比，该方法在视觉感知和客观评估方面均具有出色的性能。具体而言，这项研究表明分别比CUFS和CUFSF数据集上的大多数比较方法更高的FSIM值（0.7345和0.7080）和酷航值（0.5317和0.5091）。