Archive > Volume 19 Issue 4 > 2023,19(4):248-251. DOI:https://doi.org/10.1007/s11801-023-2168-0 CSTR:[cstr] Prev Next
Unsupervised model-driven neural network based image denoising for transmission line monitoring
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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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