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http://dx.doi.org/10.6109/jkiice.2020.25.2.327

Performance of Denoising Autoencoder for Enhancing Image in Shallow Water Acoustic Communication  

Jeong, Hyun-Soo (Department of Information and Communications Engineering, Pukyong National University)
Lee, Chae-Hui (Department of Information and Communications Engineering, Pukyong National University)
Park, Ji-Hyun (Institute of Acoustic and Vibration Engineering, Pukyong National University)
Park, Kyu-Chil (Department of Information and Communications Engineering, Pukyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.2, 2021 , pp. 327-329 More about this Journal
Abstract
Underwater acoustic communication channel is influenced by environmental parameters such as multipath, background noise and scattering. Therefore, a transmitted signal is influenced by the sea surface and the sea bottom boundaries, and a received signal shows a delay spread. These factors create a noise in the image and degrade the quality of underwater acoustic communication. To solve these problems, in this paper, we evaluate the performance of an underwater acoustic communication model using a denoising auto-encoder used for unsupervised learning. Noise images generated by the underwater multipath channel were collected and used as training data. Experimental results were analyzed as a PSNR parameter that expressed the noise ratio of the two images.
Keywords
Underwater acoustic communication; Shallow water; Multipath channel; Denoising autoencoder;
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Times Cited By KSCI : 2  (Citation Analysis)
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