Thresholding-Based Image Denoising Using Two-Dimensional S-Transform

Luan, Yu-huan; Sun, Fengzhong; Babyn, Paul; Song, Shangling; Yao, Guihua; Zhang, Yun

doi:10.1109/scet.2012.6341906

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…The threshold parameter (λ) is chosen based on the signal energy and the noise variance

λ = c σ \sqrt{2 log N},

where σ 2 is the noise variance, N is the length of the signal, and c is a threshold factor between 0 and 1 (Parolai ; Luan et al . ; Gaci ; Akram et al . ; Al‐Aboosi and Sha'ameri ).…”

Section: Theorymentioning

confidence: 99%

“…A carefully chosen c can improve λ, which, as a result, enhances filter performance in low‐signal‐to‐noise ratio (S/N) waveforms (Luan et al . ; He et al . ; Al‐Aboosi and Sha'ameri ).…”

Section: Theorymentioning

confidence: 99%

“…where σ 2 is the noise variance, N is the length of the signal, and c is a threshold factor between 0 and 1 (Parolai 2009;Luan et al 2012;Gaci 2014;; Al-Aboosi and Sha'ameri 2017). In the time-frequency filtering, the threshold (λ) is a key parameter, as shown in the thresholding functions (equations (8) to (10)).…”

Section: Time-frequency Thresholdingmentioning

confidence: 99%

“…The threshold factor (c) is chosen empirically. A carefully chosen c can improve λ, which, as a result, enhances filter performance in low-signal-to-noise ratio (S/N) waveforms (Luan et al 2012;He et al 2015; Al-Aboosi and Sha'ameri 2017). Figure 1 describes the denoising workflow, which is also summarised as follows.…”

Section: Time-frequency Thresholdingmentioning

confidence: 99%

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An application of waveform denoising for microseismic data using polarization–linearity and time–frequency thresholding

Akram¹

2018

Geophysical Prospecting

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Noise suppression or signal‐to‐noise ratio enhancement is often desired for better processing results from a microseismic dataset. In this paper, a polarization–linearity and time–frequency‐thresholding‐based approach is used for denoising waveforms. A polarization–linearity filter is initially applied to preserve the signal intervals and suppress the noise amplitudes. This is followed by time–frequency thresholding for further signal‐to‐noise ratio enhancement in the S transform domain. The parameterisation for both polarization filter and time–frequency thresholding is also discussed. Finally, real microseismic data examples are shown to demonstrate the improvements in processing results when denoised waveforms are considered in the workflow. The results indicate that current denoising approach effectively suppresses the background noise and preserves the vector fidelity of signal waveform. Consequently, the quality of event detection, arrival‐time picking, and hypocenter location improves.

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“…The threshold parameter (λ) is chosen based on the signal energy and the noise variance

λ = c σ \sqrt{2 log N},

where σ 2 is the noise variance, N is the length of the signal, and c is a threshold factor between 0 and 1 (Parolai ; Luan et al . ; Gaci ; Akram et al . ; Al‐Aboosi and Sha'ameri ).…”

Section: Theorymentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%