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多分支自相似遥感超分辨率生成对抗网络

刘佳嘉 林俊逸

现代电子技术2026,Vol.49Issue(2):65-72,8.
现代电子技术2026,Vol.49Issue(2):65-72,8.DOI:10.16652/j.issn.1004-373x.2026.02.011

多分支自相似遥感超分辨率生成对抗网络

Multi-branch self-similar remote sensing super-resolution generative adversarial network

刘佳嘉 1林俊逸1

作者信息

  • 1. 中国民用航空飞行学院 航空电子电气学院,四川 广汉 618307
  • 折叠

摘要

Abstract

In allusion to the image distortion caused by edge blurring and artifacts in images obtained by the super-resolution reconstruction of the remote sensing imagery,a new remote sensing super-resolution reconstruction algorithm is proposed.In the algorithm,a multi-branch residual dense block(MRDB)that incorporates a joint loss function,and simultaneously employs a self-similarity feature extraction module to repair its high-frequency and edge information is designed.MRDB can improve the multi-branch structure based on RRDB,which can effectively process information of different frequencies,enhance the detail restoration effect and semantic balance of images,and reduce the problem of object edge blurring.The innovative combination of multi-branch structures and dense blocks can stably extract deep features and effectively eliminate artifacts.A joint loss function is designed,which combines L1 content loss,perceptual loss,texture loss,adversarial loss,and self-similarity loss to ensure the overall clarity of the image.The comparative experiments and ablation experiments are conducted on MRDGAN.The experimental results show that in the UC Merced dataset,the qualitative effect of MRDGAN under the categories of highway,airport,and building is closer to original image.Moreover,the average PSNR is 1.13 dB higher than that of the ESRGAN algorithm,the SSIM is 0.028 5 higher,the FID is reduced by 22.13,and the CLIPscore is increased by 0.036 1.This algorithm not only can remove generated artifacts and improve the accuracy of edge reconstruction,but also can demonstrate better results in various evaluation metrics.

关键词

生成对抗网络/遥感影像/超分辨率重构/多分支结构/残差密集块/特征提取

Key words

generative adversarial network/remote sensing image/super-resolution reconstruction/multi-branch structure/residual dense block/feature extraction

分类

信息技术与安全科学

引用本文复制引用

刘佳嘉,林俊逸..多分支自相似遥感超分辨率生成对抗网络[J].现代电子技术,2026,49(2):65-72,8.

基金项目

四川省科技厅项目(24KPZP0034) (24KPZP0034)

中央高校基本科研业务费专项资金(J2023-024) (J2023-024)

现代电子技术

1004-373X

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