电力系统保护与控制2024，Vol.52Issue(3)：119-127,9.DOI:10.19783/j.cnki.pspc.230687
基于改进边缘注意力生成对抗网络的电力设备热成像超分辨率重建
Super-resolution reconstruction of thermal imaging of power equipment based on improved edge-attentive generative adversarial networks
摘要
Abstract
A super-resolution reconstruction method based on improved edge-attention generation adversarial network is proposed for low-resolution thermal imaging images of power equipment.First,using edge attention,a dual attention(DA)module of channel and position attention is introduced to capture the dependencies between different positions of the feature map and between different channels.The two sets of dependencies are fused to increase the degree of global information extraction.Then,to address the problem that the parametric rectified linear unit(PReLU)activation function performs undifferentiated activation on the neurons in the network,which leads to the limited feature expression capability of the network.The improved β-ACONC function is used to replace the PReLU function and selectively activate the neurons on the basis of identifying the effective features in order to strengthen effective features and weaken the ineffective features,and enhance the adaptive activation and feature expression capabilities of the network.Finally,the proposed improved edge-attention generative adversarial network(EA-GAN)model is experimentally validated.The results show that compared with BiCubic and the original EA-GAN model,the proposed improved model has the best network performance,the highest reconstructed image quality,and the best objective evaluation indices of peak signal-to-noise ratio(PSNR),structural similarity(SSIM)and mean square error loss(MSE-loss)mean values.These are universal in the field of infrared image reconstruction of power equipment and have a certain engineering application value.关键词
热成像/超分辨率重建/注意力机制/自适应激活函数Key words
thermal imaging/super-resolution reconstruction/attention mechanism/adaptive activation function引用本文复制引用
王艳,连洪钵,王寅初,康磊,赵洪山..基于改进边缘注意力生成对抗网络的电力设备热成像超分辨率重建[J].电力系统保护与控制,2024,52(3):119-127,9.基金项目
This work is supported by the National Natural Science Foundation of China(No.51807063). 国家自然科学基金项目资助(51807063) （No.51807063）
