An ice-covered transmission line image denoising method fused with multiple features
To address the image noise in monitoring the ice-covered state of transmission lines based on images,this paper proposes an ice-covered transmission line image denoising method fused with multiple features(MF-TLID).The algorithm consists of residual attention fusion module,source feature fusion module and feature enhancement module.The cascaded residual structure and hybrid attention are employed in the residual attention fusion module,which not only contributes to feature information mapping but also enhances the expression of feature information.The source features are fused in different feature layers of the network to retain the low-frequency information of the images,which helps improve the clarity of the image.In the feature enhancement module,both local and global features are combined,and the effective feature vector representation is learned by the feature attention weighting to improve the model removal ability.We propose a joint loss function of Charbonnier loss and Perceptual Loss,taking into account the error of pixel level and the improvement of perceptual quality.On the transmission line icing dataset,the standard deviation of Gaussian noises are between 10-40,20-50 and 30-60,PSNR and SSIM reaches {31.015 dB,29.262 dB,27.717 dB } and {0.956,0.943,0.930} respectively.Our results indicate the proposed method performs better than the mainstream denoising methods,showing stronger noise suppression ability and robustness.
ice-covered transmission lineimage denoisingfeature fusionattention mechanismjoint loss function
国家科技期刊平台
NSTL
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