电力系统保护与控制2025，Vol.53Issue(2)：89-99,11.DOI:10.19783/j.cnki.pspc.240741
考虑局部纹理特征和全局温度分布的电力设备红外图像超分辨率重建方法
Super-resolution reconstruction method for infrared images of power equipment considering local texture features and global temperature distribution
摘要
Abstract
To address the problem of low resolution in reconstructed image due to the lack of consideration for local texture features and global temperature distribution in traditional super-resolution reconstruction methods for infrared images of power equipment,a super-resolution reconstruction method based on Transformer-GAN aggregation network is proposed.Firstly,a local feature extraction module for power equipment is designed based on shift convolution,which expands the receptive field of the convolution without increasing parameters,extracting local texture and surrounding spatial dimension features of the power equipment.Then,a global feature extraction module is introduced to capture the correlation of temperature distributions between different regions of the image through deep convolution and spatial attention mechanisms.Finally,the UNet encoder-decoder network is used to fuse the local features and global representations at each layer,generating clear and natural infrared images of power equipment.The case study results show that the proposed method outperforms other methods in terms of peak signal to noise ratio(PSNR),structural similarity(SSIM),and visual information fidelity(VIF).It also has good subjective visual effects and strong generalization ability.关键词
电力设备/红外图像/超分辨率重建/局部纹理特征/全局温度分布/Transformer-GANKey words
power equipment/infrared image/super-resolution reconstruction/local texture features/global temperature distribution/Transformer-GAN引用本文复制引用
赵洪山,王惠东,刘婧萱,杨伟新,李忠航,林诗雨,余洋,吕廷彦..考虑局部纹理特征和全局温度分布的电力设备红外图像超分辨率重建方法[J].电力系统保护与控制,2025,53(2):89-99,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.52077078). 国家自然科学基金项目资助(52077078) （No.52077078）
国家电网公司科技项目资助(52018K22001P) （52018K22001P）
