Seismic Random Noise Reduction Using Adaptive Threshold Combined Scale and Directional Characteristics of Shearlet Transform

Liu, Jicheng; Gu, Yu; Chou, Yongxin; Gu, Jianfei

doi:10.1109/lgrs.2019.2949806

Cited by 29 publications

(4 citation statements)

References 24 publications

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“…Gomathi et al [22] proposed a new bivariate shrinkage function for remote sensing image denoising. Liu et al [23] proposed a method with an adaptive threshold based on scale and directional characteristics in shearlet domain to denoise. In this paper, we use the statistical relationship between WGN coefficients and high-frequency sub-bands to propose a GOF test denoising model in the shearlet domain and perform GOF test on the coefficients to remove noise.…”

Section: A Model-based Denoising Methodsmentioning

confidence: 99%

Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform

Cheng,

Chen

2023

IEEE Access

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Model-based image denoising methods are well suited for use as image processors in remote sensing systems such as satellites due to their well-developed mathematical theory and low computational cost, but these methods can often only deal with a single type of random noise. In this paper, based on the model-based image denoising techniques, a remote sensing image mixed noise denoising algorithm using Gaussian curvature in the image surface and shearlet transform is proposed. The Gaussian curvature filtering (GCF) is used to suppress the salt & pepper noise (SPN) in the image to get the first denoised image. The second denoised image is obtained by processing the coefficient matrices of the shearlet transform decomposition using statistical method and adaptive median filtering (AMF). Finally, the reconstructed image is again optimized by AMF to eliminate the residual SPN. Specially, we propose the goodness-of-fit (GOF) test denoising algorithm in the shearlet domain based on empirical distribution function (EDF) statistics to solve the problem of insufficient denoising by the traditional threshold function. Our method can effectively remove the noise and improve the visibility and usability of remote sensing images. Experiment results show that our proposed method has PSNR and MSE performance improvement compared with related model-based and learning-based methods.

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Section: A Model-based Denoising Methodsmentioning

confidence: 99%

Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform

Cheng,

Chen

2023

IEEE Access

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“…Shearlet transform is a new geometric analysis method that can highlight the characteristics of image defect signals. 13 An adaptive threshold attenuation technique for seismic random noise was proposed by Liu et al 14 based on the size and direction properties of shearlet transform. By looking at the direction adaptive threshold calculation and shearlet transform decomposition scale analysis, the denoising effect was improved.…”

Section: Related Workmentioning

confidence: 99%

The algorithm for extracting surface defects from ZrO2 ceramic bearing balls using shearlet transform image enhancement

Liao,

Xia,

Liao

et al. 2024

AIP Advances

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To solve the problems of noise coverage defect and low contrast between the defect and the background of ZrO2 ceramic bearing balls, a surface defect extraction algorithm based on shearlet transform image enhancement for ZrO2 ceramic bearing balls is proposed. According to the shape characteristics of ceramic bearing balls, the surface defect image acquisition platform is built to collect and analyze surface defect images. Gaussian filtering weakens the scatter-particle noise in the image, and the threshold corrects the coefficient generated by the shearlet transform. After shearlet transform, the relatively low-frequency and high-frequency parts appear. The low-frequency part reflects the edge information of defects, and the high-frequency part reflects the edge and texture information of defects. Thus, the integrity of the defect is ensured, and an enhanced surface defect image is obtained. The gray histogram of the enhanced image is observed. The optimal threshold is selected by the histogram threshold segmentation method, and the process of defects being completely extracted from the background is realized. Experimental results showed that the extraction rates of pits, scratches, and cracks in ZrO2 ceramic bearing balls’ surface images are 95.00%, 92.50%, and 92.50%, respectively.

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“…Nonetheless, it is challenging to determine the optimal reconstruction strategy for the seismic records in low-SNR conditions, resulting in severe residual noise and signal amplitude loss . Besides, sparse transform methods, including but not limited to curvelet transform (Herrmann et al, 2008), shearlet (Liu et al, 2019), seislet (Liu et al, 2015), and dictionary learning method (Chen et al, 2016), are proposed to suppress the complex seismic noise. The basic principle for these methods is to take advantage of the differences within the sparse properties to recover reflection signals from the field noisy records.…”

Section: Introductionmentioning

confidence: 99%

A multi-scale dense-connection denoising network for DAS-VSP records

Dong

Lin

et al. 2023

Front. Earth Sci.

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Due to high spatial resolution, low cost, and wide bandwidth, distributed optical fiber acoustic sensing (DAS) is regarded as a potential tool for data acquisition in vertical seismic profile (VSP) surveys. However, in real DAS-VSP records, desired signals are often seriously plagued by various noise, which does not appear in the conventional seismic data received by electronic geophones. Exploring a high-performing attenuation method for the background noise can significantly improve the quality of DAS-VSP records and has essential impacts on the following imaging and interpretation. Deep-learning-based methods, especially convolutional neural network (CNN), have shown remarkable performance in seismic data denoising. However, the conventional CNN-based methods may degrade when dealing with DAS-VSP records in low signal-to-noise ratio. In this study, we propose a novel multi-scale dense-connection denoising network (MDD-Net) to achieve high-accuracy processing of the complex DAS background noise. Unlike conventional multi-scale networks, MDD-Net utilizes widen convolution block to capture the multi-scale features of the analyzed data. On this basis, dense connection operations are employed to fuse the features and improve the network efficiency. Meanwhile, an enhanced spatial attention (ESA) block is designed to reinforce the features, which are helpful for noise suppression and weak signal recovery. Both synthetic and field DAS-VSP records are processed to verify the effectiveness of MDD-Net. Meanwhile, we also compare the denoising results with other competing methods. The experimental results demonstrate that MDD-Net can significantly attenuate the complex DAS background noise and restore the desired signals, even for the weak upgoing signals.

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Seismic Random Noise Reduction Using Adaptive Threshold Combined Scale and Directional Characteristics of Shearlet Transform

Cited by 29 publications

References 24 publications

Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform

Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform

The algorithm for extracting surface defects from ZrO2 ceramic bearing balls using shearlet transform image enhancement

A multi-scale dense-connection denoising network for DAS-VSP records

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