A new multi-scale framework for convolutive blind source separation

Belaid, Samir; Hattay, Jamel; Naanaa, Wady; Aguili, Taoufik

doi:10.1007/s11760-016-0877-6

Cited by 11 publications

(7 citation statements)

References 20 publications

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“…This result follows from the fact that the sources must satisfy statistical independence to allow FastICA methods to achieve high-quality separation results. Figures 11,12,13,14,15,16,17, and 18 plot an extensive set of simulation results measured with a wide range of noise levels. These results compare the separation quality of the tested images using various evaluation metrics including SNR, PSNR, RMSE, and NCC.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…In the work of [16], feedback sparse component analysis of image mixture was developed to extract the image sources by utilizing a feedback mechanism and sparse component analysis (SCA). In [17], a wavelet packet transform method was proposed in combination with a geometric de-mixing algorithm. It decomposes the mixed images by a wavelet transform (WT) and then uses the most relevant component as an input to its de-mixing geometric algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Blind image separation using pyramid technique

Abbass

Kim

2018

J Image Video Proc.

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Signal and image separation is an important processing step for accurate image reconstruction, which is increasingly applied to many medical imaging applications and communication systems. Most of the conventional separation approaches are based on frequency domain and time domain. These approaches, however, are sensitive to noise and thus often produce undesirable results. In this paper, we propose a novel method of image separation. It incorporates the property of pyramid component extracted from the image and a finite ridgelet transform (FRT) to obtain a precise analysis of the images and thus correctly separate the images even in a highly noisy environment. We obtain the multiple components of the target images by employing a pyramid processing, which operates in the various domains and thus can decompose the image into multiple components. In addition, the pyramid decomposition in the proposed method can eliminate information redundancy in the target image and thus can substantially enhance the quality of image separation. We have conducted extensive simulations, which demonstrate that the proposed pyramid structure with FRT outperforms the conventional methods based on time domain and trigonometric transforms.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Blind image separation using pyramid technique

Abbass

Kim

2018

J Image Video Proc.

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“…Blindly and via the BSS, the principle of the solution is based on hypotheses concerning the mixing system as well as hypotheses on the nature of the source signals. Related to the type of mixing system we find in the literature proposals that deal with the linear problem for the instantaneous case [11] and also others that deal with the convolutive case [12][13][14] as we also find solutions that deal with the nonlinear problem [15]. Regarding the assumptions of the source signals, the major tool applied to solve the BSS problem is called independent component analysis (ICA) where the solutions that apply this technique, are based on the concept of statistical independence of the signals, to recover the original sources.…”

Section: Introductionmentioning

confidence: 99%

Source recovery by analytical maximization of phase-shifted kurtosis for the mixtures of noisy and noiseless signals

SMATTI

Arar

2023

Preprint

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This manuscript presents a work that provides a study as well as a simple analytical solution for solving the blind source separation problem (BSS) for noiseless and noisy linear mixing of statistically independent stationary and nonstationary signals. The study is based on the exploitation of the probabilistic characteristics of the mixed signals by using the statistics of the second order and the fourth order for the completion of the separation. The proposed solution consists mainly of two steps based on the concept of the geometric solution. For the case of the mixture of two sources (2×2), the first step aims to transform the dependent signals into orthogonal signals (whitening) via the principal component analysis (PCA) principle. After the application of the PCA and in order to complete the statistical independence of the two uncorrelated signals, the second step aims to determine an adequate rotating angle that leads directly to the separation, and this angle is determined in this work analytically by the simple calculation of the phase shift of a sinusoidal objective function based on the sum of the kurtosis of the whitened signals. In the case of several sources (n×n), the solution (2×2) can be applied by a simple generalization which leads to the global separation. Whether for the noisy or noiseless case, the results obtained prove the reliability and efficiency by applying this analytical solution to achieve the desired objective, in particular by comparing the proposed algorithm with the application of two other separation algorithms, one of which involves the application of optimization techniques

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“…Ge and Jiang [9] proposed a framework based on joint blind source separation (JBSS) in order to solve the jamming suppression problem in the noise environment for the distributed radar with single transmitter and multiple receivers, where the multiple jamming enter into all the receivers through the main beam of the antennas. Belaid et al [45] proposed a new multiscale decomposition algorithm which enables the blind separation of convolutely mixed images. All the above algorithms show good separation performance when separating noiseless mixed signals.…”

Section: Introductionmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

International Journal of Rotating Machinery

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The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

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A new multi-scale framework for convolutive blind source separation

Cited by 11 publications

References 20 publications

Blind image separation using pyramid technique

Blind image separation using pyramid technique

Source recovery by analytical maximization of phase-shifted kurtosis for the mixtures of noisy and noiseless signals

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

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