Structure-Oriented DTGV Regularization for Random Noise Attenuation in Seismic Data

Wang, Dehua; Gao, Jinghuai; Liu, Naihao; Jiang, Xiudi

doi:10.1109/tgrs.2020.3001141

Cited by 18 publications

(3 citation statements)

References 40 publications

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“…In this subsection, we first use the standard test image as shown in Fig. 4(a) to present the choice scheme of parameters involved in this test, and then compare the proposed method with the related methods like TV, LLT, TGV, FTV and structure-oriented directional total generalized variation (SODTGV) denoising method [50] in different noise standard deviations σ = 10, σ = 20, σ = 30.…”

Section: A Synthetic Image Experimentsmentioning

confidence: 99%

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A Spatially Adaptive Edge-Preserving Denoising Method Based on Fractional-Order Variational PDEs

Wang

Nieto

et al. 2020

IEEE Access

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Image denoising is a basic problem in image processing. An important task of image denoising is to preserve the significant geometric features such as edges and textures while filtering out noise. So far, this is still a problem to be further studied. In this paper, we firstly introduce an edge detection function based on the Gaussian filtering operator and then analyze the filtering characteristic of the fractional derivative operator. On the basis, we establish the spatially adaptive fractional edge-preserving denoising model in the variational framework, discuss the existence and uniqueness of our proposed model solution and derive the nonlinear fractional Euler-Lagrange equation for solving our proposed model. This forms a fractional order extension of the first and second order variational approaches. Finally, we apply the proposed method to the synthetic images and real seismic data denoising to verify the effectiveness of our method and compare the experimental results of our method with the related state-of-the-art methods. Experimental results illustrate that our proposed method can not only improve the signal to noise ratio (SNR) but also adaptively preserve the structural information of an image compared with other contrastive methods. Our proposed method can also be applied to remote sensing imaging, medical imaging and so on.

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Section: A Synthetic Image Experimentsmentioning

confidence: 99%

“…The other parameters involved in this test are set as k = 0.05, ∆t = 0.1. More details about the selection of the parameters involved in the other contrastive methods are referred to the related references [14], [28], [32], [33] and [50].…”

Section: A Synthetic Image Experimentsmentioning

confidence: 99%

A Spatially Adaptive Edge-Preserving Denoising Method Based on Fractional-Order Variational PDEs

Wang

Nieto

et al. 2020

IEEE Access

Self Cite

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“…The eigenvector filter uses the Kahrunen-Loeve theory to decompose trace data into eigenimages using eigenvectors. The use of the Kahrunen-Loeve (K-L) method for seismic signals has been considered by Shukla & Jaiswal (2017), and most recently by Wang et al (2020). In the field of image processing, the K-L transformation is commonly applied in data transmission and data analysis (Zaharov et al, 2014) as well as digital image enhancement (Sharma et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Advanced Processing of 2D Marine Reflection Seismic Data Using the Common Reflection Surface (CRS) Stack Method with K-L Filter Application

Zein,

Rasimeng,

Wijaksono

2023

Geocelebes

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Data processing using the seismic reflection method is an important stage in the exploration of natural resources and minerals. This research was conducted to determine the effective and efficient stacking and filtering methods in reconstructing the subsurface geological structure of the earth from the results of data processing using ProMAX software. The data processing method used is the conventional stack and the Common Reflection Surface (CRS) stack. Aperture values of 0 ms – 50 m and 3000 ms – 150 m in the CRS stack process produce the most optimum seismic sections. Both methods produce a different quality of seismic cross-section display. The 2D cross-section model from the conventional stack method looks noisier than the results from the CRS stack method. In addition, the reflector pattern on the cross-section of the results of the CRS stack method is clearer and visible with a relatively large amplitude compared to the results of the conventional stack method. To maximize the quality of data display, data enhancement is applied, which is the K-L filter. The eigenimages value of 0.10% on the K-L filter with a horizontal window width of 120 is used to reduce random noise. Thus, an increase in the S/N ratio will be obtained in the seismic data so that the 2D cross-sectional model of the seismic reflection method can approach the original conditions of the subsurface geological structure.

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A novel approach for seismic signal denoising using optimized discrete wavelet transform via honey badger optimization algorithm

K.,

Hota,

Karras

2023

Journal of Applied Geophysics

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Structure-Oriented DTGV Regularization for Random Noise Attenuation in Seismic Data

Cited by 18 publications

References 40 publications

A Spatially Adaptive Edge-Preserving Denoising Method Based on Fractional-Order Variational PDEs

A Spatially Adaptive Edge-Preserving Denoising Method Based on Fractional-Order Variational PDEs

Advanced Processing of 2D Marine Reflection Seismic Data Using the Common Reflection Surface (CRS) Stack Method with K-L Filter Application

A novel approach for seismic signal denoising using optimized discrete wavelet transform via honey badger optimization algorithm

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