Abstract
The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. Aiming at the low resolution problem of fine-grained fittings image of trans-mission line, an image super-resolution reconstruction algorithm based on compressed sensing is proposed. The K-SVD (K-Singular Value Decomposition) algorithm is used to implement sparse representation according to the theory of compressed sensing. The reconstruction is performed by OMP (Orthogonal Matching Pursuit) algorithm. The proposed algorithm has good de-noising effect and shortened processing time. The fine-grained fit-tings image that has correlation with the reconstructed image is trained to enhance the reconstruction effect and is used for high-quality recovery of the fine-grained fittings image of the transmission line. The simulation results verify the effectiveness of the proposed algorithm, and the reconstructed image has a better improvement in subjective visual effects and objective evaluation indicators.
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Acknowledgements
The work was supported by State Grid Corporation of China Headquarters Project (5455HJ180017) named ‘Applicability Research regarding the Use of AI-based Image Recognition in Power Transmission and Transforming Inspection’.
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Wu, P., Li, C., Lin, G., Luo, J., Yang, H. (2020). Super-Resolution Reconstruction of Fine-Grained Fittings Image of Transmission Line Based on Compressed Sensing. In: Xue, Y., Zheng, Y., Rahman, S. (eds) Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control. Lecture Notes in Electrical Engineering, vol 585. Springer, Singapore. https://doi.org/10.1007/978-981-13-9783-7_80
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DOI: https://doi.org/10.1007/978-981-13-9783-7_80
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