Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint

Huang, Guangtan; Zhao, Qiang; Chen, Wei; Chen, Yangkang

doi:10.1111/1365-2478.13032

Cited by 32 publications

(5 citation statements)

References 61 publications

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“…The solution based on the L2‐norm misfit function is unstable, where a large number of outliers are introduced with much signal loss, and the difference between calculated and numerically secondary sources is noticeable. Figure 2d–f shows the deblended primary, secondary sources and estimation errors using the iterative structure‐oriented space‐varying median filtering (SOSVMF) (Huang et al., 2021); the separation effect is good, accompanied by edge loss.…”

Section: Synthetic Data Examplesmentioning

confidence: 99%

Simultaneous random plus outlier attenuation and deblending based on L1‐norm misfit function

Sun

Cao

Chen

et al. 2023

Geophysical Prospecting

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Deblending technology aims at separating simultaneous source seismic data between adjacent shots by allowing multiple sources to be shot simultaneously. Conventional deblending methods based on sparse inversion assume that the primary source is coherent, and the secondary source is randomized. The L2-norm minimization constraint can effectively minimize the Gaussian random noise while deblending in the transform domain. Nonetheless, the L2-norm misfit function is highly sensitive to outliers, negatively influencing the deblending performance. An effective optimization strategy is developed with deblending in pre-stack seismic data to eliminate outliers and enhance deblending accuracy. For this reason, we introduce the deblending algorithm in the morphological component analysis framework, modify the L2-norm misfit function to outlier-robust L1-norm and provide the corresponding derivation in detail via the alternating direction method of multipliers. Applications to synthetic and field data sets prove the improved robustness and efficiency of our deblending method.

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Section: Synthetic Data Examplesmentioning

confidence: 99%

Simultaneous random plus outlier attenuation and deblending based on L1‐norm misfit function

Sun

Cao

Chen

et al. 2023

Geophysical Prospecting

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show abstract

“…Traditional denoising methods include spatial domain denoising methods [1] and transform domain denoising methods [7], which mostly appear in the early stages. For example, Donoho and John Stone [30] proposed a wavelet threshold denoising method; Huang et al proposed that the median filtering method was the most widely used method [31]; Saito et al [32] designed and used the LMMSE filter to improve the denoising effect by restoring image texture; and the antileakage least-squares spectral analysis proposed by Ghaderpour [33] used the Fourier transform and least squares spectral analysis to process noise in seismic data. However, traditional denoising methods often face issues such as a high complexity and poor robustness.…”

Section: Image Denoising Literature Reviewmentioning

confidence: 99%

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

et al. 2023

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In real-world scenarios, images may be affected by additional noise during compression and transmission, which interferes with postprocessing such as image segmentation and feature extraction. Image noise can also be induced by environmental variables and imperfections in the imaging equipment. Robust principal component analysis (RPCA), one of the traditional approaches for denoising images, suffers from a failure to efficiently use the background’s low-rank prior information, which lowers its effectiveness under complex noise backgrounds. In this paper, we propose a robust PCA method based on a nonconvex low-rank approximation and total variational regularization (TV) to model the image denoising problem in order to improve the denoising performance. Firstly, we use a nonconvex γ-norm to address the issue that the traditional nuclear norm penalizes large singular values excessively. The rank approximation is more accurate than the nuclear norm thanks to the elimination of matrix elements with substantial approximation errors to reduce the sparsity error. The method’s robustness is improved by utilizing the low sensitivity of the γ-norm to outliers. Secondly, we use the l1-norm to increase the sparsity of the foreground noise. The TV norm is used to improve the smoothness of the graph structure in accordance with the sparsity of the image in the gradient domain. The denoising effectiveness of the model is increased by employing the alternating direction multiplier strategy to locate the global optimal solution. It is important to note that our method does not require any labeled images, and its unsupervised denoising principle enables the generalization of the method to different scenarios for application. Our method can perform denoising experiments on images with different types of noise. Extensive experiments show that our method can fully preserve the edge structure information of the image, preserve important features of the image, and maintain excellent visual effects in terms of brightness smoothing.

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“…Historical or degraded audio recordings can benefit from time-domain filtering algorithms that aim to restore missing or damaged portions of the signal. Students can learn how to use techniques like interpolation, transient detection, or noise reduction to improve the fidelity and intelligibility of archival recordings, enabling them to study and appreciate musical works from the past [12]. Additionally, timedomain filtering techniques are utilized in audio analysis and transcription.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Audio Signal Quality and Learning Experience with Integrated Covariance Weiner Filtering in College Music Education

Yang

2023

IJRITCC

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In recent years, computer music technology has become increasingly prevalent in college music education, offering new possibilities for creative expression and pedagogical approaches. This paper concentrated on the music education in the colleges with the application of integrated time and frequency filtering (ITFF) with Kalman integrated covariance Weiner filtering in college music education. The ITFF technique combines time and frequency domain analysis to enhance the quality and clarity of audio signals. By integrating the Kalman integrated covariance Weiner filtering, the ITFF method provides robust noise reduction and improved signal representation. This integrated approach enables music educators to effectively analyze and manipulate audio signals in real-time, fostering a more immersive and engaging learning environment for students. The findings of this study highlight the benefits and potential applications of ITFF with Kalman-integrated covariance Weiner filtering in college music education, including audio signal enhancement, sound synthesis, and interactive performance systems. The integration of computer music technology with advanced filtering techniques presents new opportunities for exploring sound, composition, and music production within an educational context.

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Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint

Cited by 32 publications

References 61 publications

Simultaneous random plus outlier attenuation and deblending based on L1‐norm misfit function

Simultaneous random plus outlier attenuation and deblending based on L1‐norm misfit function

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

Enhancing Audio Signal Quality and Learning Experience with Integrated Covariance Weiner Filtering in College Music Education

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