&lt;title&gt;LUM filters for smoothing and sharpening&lt;/title&gt;

Boncelet, Charles G.; Hardie, Russell C.; Hakami, Mona; Arce, Gonzalo R.

doi:10.1117/12.44317

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…The conventional method to filter out the noise gives rise to distortion of the reflection event, as well as smearing the reflection edges. To suppress noise while preserving geologic edges, algorithms such as edge-preserving smoothing (EPS) (Boncelet et al, 1991;Bakker et al, 1999;Luo et al, 2002;AlBinHassan et al, 2006;AlDossary and Marfurt, 2007) and structure-oriented filtering (SOF) (Hocker and Fehmers, 2002) have been developed. Ma and Plonka (2007) developed an anisotropic diffusion denoising method and Ma (2007) applied curvelets to seismic edge detection.…”

Section: Introductionmentioning

confidence: 99%

Structure-oriented edge-preserving smoothing based on accurate estimation of orientation and edges

Wang

Chen

Xu³

et al. 2009

Appl. Geophys.

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In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orientation and edge of the reflections. The information is usually estimated from seismic data with full frequency bandwidth. When the data has a very low signal to noise ratio (SNR), the noise usually reduces the estimation accuracy. For seismic data with extremely low SNR, the dominant frequency has higher SNR than other frequencies, so it can provide orientation and edge information more reliably than other frequencies. Orientation and edge are usually described in terms of apparent reflection dips and coherence differences, respectively. When frequency changes, both dip and coherence difference change more slowly than the seismogram itself. For this reason, dip and coherence estimated from dominant frequency data can approximately represent those of other frequency data. Ricker wavelet are widely used in seismic modeling. The Marr wavelet has the same shape as Ricker wavelets in both time and frequency domains, so the Marr wavelet transform is selected to divide seismic data into several frequency bands. Reflection apparent dip as well as the edge information can be obtained by scanning the dominant frequency data. This information can be used to selectively smooth the frequency bands (dominant, low, and high frequencies) separately by structure-oriented edge-preserving smoothing technology. The ultimate noise-suppressed seismic data is the combination of the smoothed frequency band data. Application to synthetic and real data shows the method can effectively reduce noise, preserve edges, improve trackable reflection continuity, and maintain useful information in seismic data.

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Section: Introductionmentioning

confidence: 99%

Structure-oriented edge-preserving smoothing based on accurate estimation of orientation and edges

Wang

Chen

Xu³

et al. 2009

Appl. Geophys.

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“…Conventional filtering methods such as Gauss filtering, median filtering and Frequency‐Wavenumber domain filtering etc (Boncelet et al ., 1991; Bakker et al ., 1999; Luo et al ., 2002; AlBinHassan et al ., 2006; Al‐Dossary et al., 2007; Wang Jun et al., 2009; Hale, 2011a; Saleh Al‐Dossary et al., 2016) usually blur the structural information of seismic data while denoising, so it is necessary to find a filtering method that can maintain the edge information of faults and other geologic bodies.…”

Section: Introductionmentioning

confidence: 99%

Anisotropic diffusion filtering based on fault confidence measure and stratigraphic coherence coefficients

Wang

Zhang

Yang

et al. 2021

Geophysical Prospecting

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We develop an algorithm for seismic data filtering to preserve the boundary information of faults and other geological bodies while suppressing the noise. Based on the theory of anisotropic diffusion filtering, this method explores the relationship between eigenvalues of the structure tensor and local structural features of three‐dimensional seismic images and innovatively introduces the definition of stratigraphic coherence coefficients. Then we propose a new system to design the eigenvalues of the diffusion tensor by using the fault confidence measure and stratigraphic coherence coefficients, which can control the filtering intensity of seismic data in different orientations. The value of fault confidence measure is close to 0 where there are flat continuous reflectors and the intensity of diffusion is strong. On the contrary, the diffusion is very weak within presumptive fault zones. Results of both synthetic model and real data prove that the proposed method can effectively suppress noise, preserve faults well and enhance the continuity of the reflectors, which can provide basic data with high signal‐to‐noise ratio for subsequent seismic interpretation.

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“…For example, order statistic filters such as the Alpha-Trimmed Meanfilter (ATM) [7], the Lower-Upper-Middle filter (LUM) [8] and the Permutation Weighted Median filter [9] can remove the different types of noise while enhancing the edges and fine details. Those algorithms are very simple, but show the ability for a limited noise.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Filtering for Color Image Sharpening and Denoising

Horiuchi

Watanabe

Tominaga

2007

14th International Conference of Image Analysis and Processing - Workshops (ICIAPW 2007)

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Both tasks of sharpening and denoising color images are eternity problems for improving image quality. This paper proposes a filtering algorithm which brings a dramatic improvement in sharpening effects and reducing flat-area noises for natural color images. First, we generate a luminance slope map, which classifies a color image into the edge areas and the flat areas with uniform color. Second, the Gaussian derivative filter is applied to sharpening each segmented edge area. In order to keep color balance, the edge-sharpening filter is applied only to the luminance component of the color image. In the flat areas, a filter based on the SUSAN algorithm is applied to reducing the background noises with preserving sharpened edges. The performance of the proposed method is verified for natural image with Gaussian noise and impulse noise.

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<title>LUM filters for smoothing and sharpening</title>

Cited by 7 publications

References 8 publications

Structure-oriented edge-preserving smoothing based on accurate estimation of orientation and edges

Structure-oriented edge-preserving smoothing based on accurate estimation of orientation and edges

Anisotropic diffusion filtering based on fault confidence measure and stratigraphic coherence coefficients

Adaptive Filtering for Color Image Sharpening and Denoising

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