Comparisons of wavelets, contourlets and curvelets in seismic denoising

Shan, H. G.; Ma, Jianwei; Yang, Huizhu

doi:10.1016/j.jappgeo.2009.08.002

Cited by 112 publications

(36 citation statements)

References 48 publications

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“…The ROI is reconstructed from the coefficients whose low-frequency values are erased. Shan et al [39] compared wavelet, curvelet, and contourlet transform for denoising seismic waves. They stated that curvelet and contourlet transforms give better results than wavelets.…”

Section: Low-frequency Eliminationmentioning

confidence: 99%

Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Kus

Karagöz²

2013

Turk J Elec Eng & Comp Sci

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A fully automated method for detecting microcalcification (MC) clusters in regions of interest (ROIs) extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, In particular, a true positive rate of about 94% is achieved at the rate of 0.06 false positives per image.

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Section: Low-frequency Eliminationmentioning

confidence: 99%

Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Kus

Karagöz²

2013

Turk J Elec Eng & Comp Sci

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“…Curvelets and ridgelets, same as DWT, their construction is not associated with a multiresolution analysis. This and other issues make the discrete implementation of curvelets very challenging as claimed in [22], therefore two different implementations of it have been suggested [20] and [23]. In an attempt to provide a better discrete implementation, The Contourlet transform was developed as an improvement over wavelet and Curvelet and ridgelets [21].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the proposed method has better imperceptibility when compared with other Contourlet based watermarking techniques. Although Contourlet aims to better capture the directionality of the image features, this is still insufficient and causing visual artifacts into the host image, which is not a desirable property in applications such as watermarking [23].…”

Section: Introductionmentioning

confidence: 99%

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Ahmaderaghi

Kurugöllü

Rincón

et al. 2018

IEEE Trans. Comput. Imaging

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“…A variety of noise can be introduced during seismic data acquisition by various sources, and this noise will then have strong interference on the signal recovered for seismic exploration. Therefore, noise removal plays an important role in increasing the quality of seismic exploration data, and various noise removal methods have been designed according to the characteristics of the noise (Cao and Chen ; Shan, Ma, and Yang ; Elboth, Presterud, and Hermansen ; Tang and Ma ; Baddari et al . ; Oropeza and Sacchi ).…”

Section: Introductionmentioning

confidence: 99%

Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic

Zhang

Lin

et al. 2015

Geophysical Prospecting

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A B S T R A C TRadial-trace time-frequency peak filtering filters a seismic record along the radialtrace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial-trace time-frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often random noise whose energy is concentrated in certain directions; the noise in these directions is correlative. We refer to this kind of random noise (that is distributed randomly in time but correlative in the space) as directional random noise. Under radial-trace time-frequency peak filtering, the directional random noise will be treated as signal and enhanced when this noise has same direction as the signal. Therefore, we need to identify the directional random noise before the filtering. In this paper, we test the linearity of signal and directional random noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional random noise is a random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional random noise by the Hurst exponent values. The directional random noise can then be suppressed by using a long filtering window length during the radial-trace timefrequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional random noise and can effectively recover the reflected events.

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Comparisons of wavelets, contourlets and curvelets in seismic denoising

Cited by 112 publications

References 48 publications

Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic

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