Single-Image Dehazing of High-Voltage Power Transmission Line Based on Unsupervised Iterative Learning of Knowledge Transfer

Xiaoyi Cuan, Kai Xie, Wei Yang, Hao Sun and Keping Wang ()
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Xiaoyi Cuan: State Grid Henan Electric Power Research Institute, Zhengzhou 450052, China
Kai Xie: State Grid Henan Electric Power Research Institute, Zhengzhou 450052, China
Wei Yang: State Grid Henan Electric Power Research Institute, Zhengzhou 450052, China
Hao Sun: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
Keping Wang: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China

Mathematics, 2025, vol. 13, issue 20, 1-18

Abstract: Single-image dehazing of high-voltage power transmission lines (HPTLs) using deep learning methods confronts two critical challenges: the non-homogeneous haze distribution in HPTL images and the unavailability of paired clear images for supervised training. To overcome these issues, this paper proposes a novel dehaze neural network, named FIF-RSCT-Net, that employs a hybrid supervised-to-unsupervised iterative learning approach according to the characteristic of HPTL single images. The FIF-RSCT-Net incorporates the Spatial–Channel Feature Intersection modules and Residual Separable Convolution Transformers to enhance the feature representation capability. Crucially, this novel architecture could learn more generalized dehazing knowledge that can be transferred from the original image domain to HPTL scenarios. In the dehazing knowledge transformation, an unsupervised iterative learning mechanism based on the Line Segment Detector is designed to optimize the restoration of power transmission lines. The effectiveness of FIF-RSCT-Net on the original image domain is demonstrated in the comparative experiments of the I-Haze, O-Haze, NH-Haze, and SOTS datasets. Our methodology achieves the best average PSNR of 24.647 dB and SSIM of 0.8512. And the qualitative evaluation of unsupervised iterative learning results shows that the missed line segments are exhibited during progressive training iterations.

Keywords: high-voltage power transmission line; single-image dehazing; unsupervised iterative learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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