基于多任务对抗和抗噪对抗学习的人脸超分辨率算法  

Face Super-Resolution Algorithm Based on Multi-task Adversarial and Antinoise Adversarial Learning

作　　者：陈泓佑 陈帆[1] 和红杰[1] 蒋桐雨 CHEN Hongyou;CHEN Fan;HE Hongjie;JIANG Tongyu(Key Laboratory of Signal and Information Processing,Sichuan Province,Southwest Jiaotong University,Chengdu 611756)

机构地区：[1]西南交通大学信号与信息处理四川省高校重点实验室,成都611756

出　　处：《模式识别与人工智能》2022年第10期863-880,共18页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.U1936113,61872303)资助。

摘　　要：高倍率单幅人脸图像超分辨率重建是一项具有实用价值但困难的任务。在人脸超分辨率任务中,端到端网络超分辨率图像较模糊,图像真实性和人眼视觉效果较差。针对上述问题,文中提出基于多任务对抗和抗噪对抗学习的人脸超分辨率算法。算法分为端到端网络学习阶段和网络参数微调阶段。为了提高端到端学习效果,设计深度多任务拉普拉斯金字塔网络,并结合多任务对抗学习。主任务为端到端学习,子任务为优化对抗学习惩罚项函数。为了改进通过对抗学习并微调主任务网络参数后的效果,在对抗学习的判别器优化过程中,融入抗噪对抗学习。实验表明,文中算法能使人脸超分辨率图像更具有图像真实性,更符合人眼视觉习惯。The super-resolution(SR)of high magnification single face image is a hard but valuable task.In the face super-resolution(FSR)task,the end-to-end network SR image is fuzzy,and the photoreality and human visual effect are poor.Aiming at the problems,a FSR algorithm based on multi-task adversarial learning(MTAL)and antinoise adversarial learning(ANAL)is proposed.The algorithm is divided into end-to-end network learning and network parameters fine-tuning.To improve the end-to-end learning result,a deep multi-task Laplacian pyramid network(MTLapNet)is designed and integrated with MTAL.The main task is end-to-end learning,while the subtask is the optimization of adversarial learning penalty function.To improve the result of adversarial learning and parameters fine-tuning of the main task network,ANAL is integrated into the optimization process of discriminator of adversarial learning.The experiments show that the proposed algorithm can make the FSR image more photo-realistic and more consistent with human visual habits.

关 键 词：深度学习 人脸超分辨率(FSR) 多任务对抗学习(MTAL) 抗噪对抗学习(ANAL) 多任务拉普拉斯金字塔网络(MTLapNet) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]