Depressing the noise effects shown in GPR images by employing the non-local approach

Chang, Hsiang Chieh; Huang, Yung-Fa; Hsu, Keng-Tsang; Lee, K. F.; Chiang, Chih‐Hung

doi:10.1109/icgpr.2016.7572650

Cited by 2 publications

(1 citation statement)

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“…Many methods have been developed for random noise reduction and reflected events recovery. Recently, Timefrequency peak filtering method [2], singular value decomposition method [3], local signal-and-noise orthogonalization [4], and dictionary method [5], have been exclusively used for GPR data denoising. Because of the high SNR of GPR signals, it is easier to denoise.…”

Section: Figure 1 Road Collapsementioning

confidence: 99%

Ground Penetrating Radar Weak Signals Denoising via Semi-soft Threshold Empirical Wavelet Transform

Qiao¹,

Feng²,

Zheng³

2019

ISI

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Ground penetrating radar (GPR) weak signals have the characteristics of low signal-to-noise ratio (SNR) and high frequency, which is a major challenge to noise attenuation. In this paper, we propose a GPR denoising approach based on empirical wavelet transform (EWT) combined with semi-soft thresholding. According to the frequency characteristics of signal, a spectrum segmentation strategy is designed. It can adaptively decompose signal and noise into different modes. The mode which contains more valid signals is processed by hard thresholding to reserve amplitude; the other modes which contain useless signals are processed by soft threshold functions to maintain the continuity of the signal. After weak signal denoising by our proposed method, we compared its performance on synthetic and field data using complete ensemble empirical mode decomposition (CEEMD) and synchro squeezed wavelet transform (SWT). The proposed method denoising performance is better than other two methods.

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Section: Figure 1 Road Collapsementioning

confidence: 99%