基于脉冲注意力机制的轻量化面部超分重建方法  

A lightweight face super-resolution reconstruction method based on pulse attention mechanism

作　　者：李娇 高磊怡 张瑞欣 吴越 邓红霞[1] LI Jiao;GAO Leiyi;ZHANG Ruixin;WU Yue;DENG Hongxia(College of Computer Science and Technology(College of Data Science),Taiyuan University of Technology,Jinzhong 030600,China)

机构地区：[1]太原理工大学计算机科学与技术学院(大数据学院),山西晋中030600

出　　处：《计算机工程与科学》2025年第3期494-503,共10页Computer Engineering & Science

基　　金：山西省中央引导地方科技发展资金(YDZJSX2022A016;YDZJSX2021C005)。

摘　　要：基于深度学习的人脸超分辨率研究近年来取得了重大进展,而如何在保证恢复面部精细自然纹理的同时限制网络模型复杂度,满足在轻量化设备上使用的需求,是该领域的一个难点。为此,提出了一种基于脉冲注意力机制的轻量化人脸超分重建方法。所提出的新型脉冲注意力机制将脉冲耦合神经网络提取的多轮次全局信息融合进窗口自注意力机制,利用全局信息和局部信息以改善方法的学习能力;采用对抗生成网络结构,构建基于窗口自注意力的渐进式生成器以保证方法的轻量化。在CelebA和Helen数据集上的实验结果表明,该方法在LPIPS和MPS感知评价指标上表现优异;与同参数量级的方法相比,该方法在所有指标上均有大幅提升,在主观视觉质量上也表现优秀。Research on face super-resolution based on deep learning has made significant progress in recent years.However,a challenging aspect in this field is how to effectively restrict model complexity while preserving fine and natural facial textural details during the restoration process,and it’s crucial to meet the demand of transferring the network model onto lightweight devices.Therefore,a lightweight face super-resolution reconstruction method based on pulse attention mechanism is proposed.The proposed new pulse attention mechanism integrates the multi-round global information extracted by the pulse-coupled neural network into the window self-attention mechanism,uses global information and local information to improve the learning ability of the network,and uses the adversarial generation network structure to build a progressive generator based on window self-attention to ensure the lightweighting of the method.Experimental results on the CelebA and Helen datasets show that this method performs excellently on LPIPS and MPS perceptual evaluation indicators.Compared with methods of the same parameter magnitude,it achieves significant improvement across all metrics and exhibits superior subjective visual quality.

关 键 词：人脸超分辨率 脉冲耦合神经网络 注意力机制 轻量化网络 生成对抗网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]