| YAO Nan,WANG Zhen,ZHANG Jun,ZHU Xueqiong,XUE Hai.Unsupervised model-driven neural network based image denoising for transmission line monitoring[J].Optoelectronics Letters,2023,(4):248-251 |
| Unsupervised model-driven neural network based image denoising for transmission line monitoring |
| Author Name | Affiliation | | YAO Nan | Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China[ This work has been supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co., Ltd: Research on Early Warning Technology of Overhead Transmission Channel External Invasion Risk Based on Layered Calculation No.J2021064. | | WANG Zhen | Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China | | ZHANG Jun | State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China[ This work has been supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co., Ltd: Research on Early Warning Technology of Overhead Transmission Channel External Invasion Risk Based on Layered Calculation No.J2021064. | | ZHU Xueqiong | Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China | | XUE Hai | Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China |
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| Abstract: |
| With the expansion of smart grid and Internet of things (IoT) technology, edge computing has a wide variety of applications in these domains. The criteria for real-time monitoring and accuracy are particularly high in the field of online real-time monitoring of electricity lines. Based on edge technology, high-quality real-time monitoring can be performed for transmission lines using image processing techniques. Therefore, we propose an image denoising method, which can learn clean images using a stream-based generative model. The stream model uses a two-stage approach in the network to handle the different training periods of denoising separately. Experimental results show that the proposed method has good denoising performance. |
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