基于GAN的人脸超分辨率重建算法研究  

Research on face super-resolution reconstruction algorithm based on GAN

作　　者：李晓萌 陈兆学[1] LI Xiaomeng;CHEN Zhaoxue(School of Medical Instrument and Food Engineering,the University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学医疗器械与食品学院,上海200093

出　　处：《光学技术》2021年第1期101-106,共6页Optical Technique

摘　　要：为解决当前人脸超分辨率算法细节处理不足和过度平滑等问题,基于对抗网络技术提出一种针对单一面部图像的超分辨率重建算法。在生成网络中并联边缘检测网络,提取丰富的人脸轮廓细节以辅助特征提取,通过Ranger优化器优化网络训练过程,最终结合客观评价和主观评价指标,建立数学模型综合评价重建效果。实验结果表明,算法较三次样条法、SRGAN、FSRCNN等方法具有更优的主观和客观评价结果。提升了面部的细节复原能力,具有更好的重建效果。Aiming at the problems of insufficient detail and over smooth in current face super-resolution algorithms,an algorithm for the Single Image Super-Resolution Reconstruction based on the Generative Adversarial Network(GAN)is proposed.The algorithm connects the edge detection network in parallel in the generation network,extracting abundant face contour details to assist in feature extraction,optimizing the network training process through the Ranger optimizer.Finally,establish a mathematical model to comprehensively evaluate the reconstruction effect combining objective assessment and subjective assessment indicators.The experimental results show that the algorithm has better subjective and objective effects than the Cubic Spline Method,SRGAN,FSRCNN,etc.It is proved that the algorithm improves the reconstruction ability of facial details and has a better reconstruction effect.

关 键 词：超分辨率技术 对抗网络 边缘增强 深度学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]