Wavelet Based De-noising Using Logarithmic Shrinkage Function

Ullah, Hayat; Amir, Muhammad; Haq, İhsan Ul; Khan, Shafqat Ullah; Rahim, M. K. A.; Khan, Khan Bahadar

doi:10.1007/s11277-017-4927-3

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…We have also evaluated the proposed filter's performance for two different pathological PCG signals of 2 ms duration taken from the PhysioNet database [ 55 , 56 ] in the presence of Gaussian noise. The proposed filter output is compared to other recently proposed cascaded filter models objectively in terms of MSE, SNR, ANR, PSNR [ 57 , 58 ], correlation coefficient (CC) [ 59 , 60 ], and Mean Absolute Error (MAE) and subjectively in terms of the output signal quality. Simulation parameters are as follows: filter length M = 2; the fixed step size used for the adaptive filter is 0.01.…”

Section: Resultsmentioning

confidence: 99%

A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal

Pauline

Dhanalakshmi

Kumar

et al. 2022

Journal of Healthcare Engineering

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Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.

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Section: Resultsmentioning

confidence: 99%

A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal

Pauline

Dhanalakshmi

Kumar

et al. 2022

Journal of Healthcare Engineering

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“…Different sparsity-based algorithms have been developed in the past to de-noise and recover sparse signals and images, that is, soft thresholding [1], hard thresholding [2], [3], [4], firm thresholding [5], non-negative garrote thresholding [6], hyperbolic tangent thresholding [7], logarithmic thresholding [8], hankel sparse low-rank approximation [9], proximal operators [10], [11], [12], alternating direction method of multipliers [13], [14], block thresholding [15], and overlapping group shrinkage (OGS) [16]. Along with these established techniques, some new techniques are also used for de-noising of specific image types.…”

Section: Introductionmentioning

confidence: 99%

“…Along with these established techniques, some new techniques are also used for de-noising of specific image types. Jawad in [7] uses hyperbolic tangent thresholding and Hayat in [8] uses logarithmic based thresholding for de-noising biomedical images. An adaptive thresholding method based on neural networks is used for de-noising of Gaussian and speckle noise in natural,…”

Section: Introductionmentioning

confidence: 99%

De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

et al. 2023

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In this work a new thresholding function referred to as 'mixture model shrinkage' (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.INDEX TERMS Convex optimization, EM algorithm, maximum a posteriori estimator (MAP), MM algorithm, PSNR, RMSE, sparse signal processing.

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“…The performance of the OFDM system is degraded and the efficiency of the transmission is reduced due impulsive noise's broad frequency component. Nowadays, the main concern of this research area is to investigate a new method to mitigate this type of noise, therefore improving systems performance in terms of [14] to eliminate the impulsive noise of corrupted images. Adaptive filters have been used successfully for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

SαS noise suppression for OFDM wireless communication in rayleight channel

Hamlili¹,

Kameche²,

Abdelmalek³

2020

IJECE

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Orthogonal frequency division multiplexing (OFDM) is a form of multi-carrier transmission technique widely used in the modern wireless network to achieve high-speed data transmission with good spectral efficiency. However, in impulsive noise environement BER performances of these systems, originally designed for a Gaussian noise model, are much degraded. In this paper, a new symmetric-alpha-stable (SαS) noise suppression technique based conjointly on adaptive modulation, convolutional coding (AMC) and Recursive Least Square (RLS) filtering is presented. The proposed scheme is applied on OFDM system in Rayleigh fading channel. The transmissions are analyzed under different combinations of digital modulation schemes (BPSK, QPSK, 16-QAM, 64-QAM) and convolutional code rates (1/2, 2/3, 3/4). Simulation results show that our proposed hybrid technique provides effective impulsive noise cancelation in OFDM system and exhibits better BER performance.

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Wavelet Based De-noising Using Logarithmic Shrinkage Function

Cited by 10 publications

References 15 publications

A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal

A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal

De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

SαS noise suppression for OFDM wireless communication in rayleight channel

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