TEM-NLnet: A Deep Denoising Network for Transient Electromagnetic Signal With Noise Learning

Wang, Mingyue; Fan, Lin; Chen, Kecheng; Luo, Wei; Qiang, Sunyuan

doi:10.1109/tgrs.2022.3148340

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Although the least-squares generative adversarial network (LSGAN) [ 22 ], the conditional generative adversarial network (CGAN) [ 23 ], and other methods were used to improve the model and achieved a better denoising effect than traditional methods, there are still problems such as slow model convergence. In References [ 9 , 10 , 11 ], the authors transformed the problem of signal denoising into that of image denoising, processed the image with CNN, and achieved good results in the field of seismic signal and transient electromagnetic (TEM) signal denoising. This method will not cause large deviation in amplitude, time, and phase information after signal denoising, but it cannot achieve perfect separation of signal and noise.…”

Section: Related Workmentioning

confidence: 99%

“…A detailed description of the signal noise reduction method based on DL will be discussed later in the second section. Although these works have achieved good results, there are still some drawbacks: (i) The perfect separation of signal and noise cannot be realized [ 9 , 10 , 11 ]; (ii) The existing noise reduction methods based on the generative adversarial network (GAN) [ 12 ] still have the problems of long training time and slow convergence speed in the training process [ 13 , 14 ]; (iii) The requirements for the number of signal sampling points are high. When the number of signal sampling points is too small and the waveform is not obvious, a satisfactory denoising effect cannot be achieved [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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A Method of Noise Reduction for Radio Communication Signal Based on RaGAN

Peng

Fang

Fan

et al. 2023

Sensors

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Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method of Noise Reduction for Radio Communication Signal Based on RaGAN

Peng

Fang

Fan

et al. 2023

Sensors

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show abstract

“…Deep learning (DL) methods have shown their superiority to traditional signal-processing algorithms in different tasks, including signal denoising [20], [21], signal classification [22], signal enhancement [23], and signal super-resolution [24]. In biomedical signal processing, Wang et al [25] proposed a denoising framework based on a conditional generative adversarial network to remove noises from baseline wander, electrode motion, and muscle artifacts in electrocardiogram (ECG) signals.…”

Section: Introductionmentioning

confidence: 99%

BSS-TFNet: Attention-Enhanced Background Signal Suppression Network for Time-Frequency Spectrum in Magnetic Particle Imaging

Wei,

Liu,

Zhu

et al. 2024

IEEE Trans. Emerg. Top. Comput. Intell.

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Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality, which uses the nonlinear response of superparamagnetic iron oxide nanoparticles to the applied magnetic field to image their spatial distribution. Background signal is the main source of artifacts in MPI, which mainly includes harmonic interference and Gaussian noise. For different sources of noise, the existing methods directly process the time domain signal to achieve signal enhancement or construct system function by frequency domain signal to obtain high-quality reconstructed images. However, due to the randomness and variety of the background signal, the existing methods fail to eliminate all kinds of noise at the same time, especially when the noise is nonlinear. In this work, we proposed a deep learning method adopting self-attention mechanism, which can effectively suppress different levels of harmonic interference and Gaussian noise simultaneously. Our method deals with the two-dimensional time-frequency spectrum acquired by short-time Fourier transform from the temporal signal, learning global features and local features between time and frequency domain through the network, to achieve the purpose of reducing background noise. The performance of our method is analyzed via simulation experiments and imaging experiments performed with an in-house MPI scanner, which shows that our method can effectively suppress background signals and obtain high-quality MPI images.

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“…Chen et al [17] proposed a deep convolutional neural network CNN-based denoiser to model the noise estimation image for different signal features. Wang et al [18] suggested employing generative adversarial networks to construct a dataset and train a deep neural network-based denoiser to learn the mapping from noisy TDEM signals to the corresponding noise-free signals. Yu et al [19] proposed the CG-DAE, a CNN-GRU Dual Auto-Encoder designed for 2-D TDEM data processing.…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

Xing,

Li,

et al. 2024

Remote Sensing

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Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques.

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TEM-NLnet: A Deep Denoising Network for Transient Electromagnetic Signal With Noise Learning

Cited by 10 publications

References 26 publications

A Method of Noise Reduction for Radio Communication Signal Based on RaGAN

A Method of Noise Reduction for Radio Communication Signal Based on RaGAN

BSS-TFNet: Attention-Enhanced Background Signal Suppression Network for Time-Frequency Spectrum in Magnetic Particle Imaging

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

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