Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise

Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise

The high-fidelity attenuation of random ground penetrating radar (GPR) noise is important for enhancing the signal-noise ratio (SNR). In this paper, a novel network structure for convolutional denoising autoencoders (CDAEs) was proposed to effectively resolve various problems in the noise attenuatio...

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Bibliographic Details
Main Authors: Deshan Feng, Xiangyu Wang, Xun Wang, Siyuan Ding, Hua Zhang
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Remote Sensing
Subjects:
GPR
noise attenuation
Gaussian spike impulse noise
deep convolutional denoising autoencoders (CDAEs)
deep convolutional denoising autoencoders with network structure optimization (CDAEsNSO)
Online Access:https://www.mdpi.com/2072-4292/13/9/1761

Internet

https://www.mdpi.com/2072-4292/13/9/1761

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