scholarly journals A Novel Technique of ECG Denoising based on Lifting Wavelet Transform and Total Variation Minimization

2020 ◽  
Author(s):  
Talbi Mourad ◽  
Baazaoui Riadh
Keyword(s):  
Wavelet Transform ◽  
Total Variation ◽  
Wavelet Coefficient ◽  
Additive White Gaussian Noise ◽  
The Other ◽  
Lifting Wavelet Transform ◽  
Denoising Method ◽  
Total Variation Minimization ◽  
Ecg Denoising ◽  
Lifting Wavelet

Abstract Buckground:The signal of Electrocardiogram (•) is one of the most popular diagnostic means providing an electrical picture of the heart and also information about different pathological conditions. Due to the path deformities and external electrical disturbances, the signal of becomes noisy. Hence, in literature, many ECG denoising algorithms have been proposed and among them we can mention the techniques based on wavelet coefficient shrinkage. The purpose of this paper is to denoise ECG signals applying a new ECG Denoising technique and proving its performance compared to some denoising approaches existing in literature. This new proposed technique of B Denoising consists at the first step in applying the Lifting Wavelet Transform (u) to the noisy c Signal (where 2 is the decomposition level) in order to obtain three noisy wavelet sub-bands, k, g and r. The two coefficients, o, u are details ones and they are denoised by soft or hard thresholding in order to obtain denoised coefficients, n1 and d2. The coefficient : is an approximation one and is denoised by Total Variation Minimization in order to obtain a denoised one, T2. Finally, the inverse of his applied to e, and s in order to obtain the denoised i signal. The evaluation of this proposed technique is performed by comparing it to three other denoising approaches existing in literature. The first one of these approaches is based on g, the second one is n double-density complex a denoising method and the third one is based on non local means.Results:All These techniques are applied on a number of lsignals taken from database and corrupted by an additive White Gaussian noise at different values of Signal to Noise Ratio (o). The obtained results from the computation of the f and the Mean Square Error ( ), show that the proposed technique outperforms the other three mentioned techniques.Conclusion:In this paper, the proposed ECG denoising technique based on E and l, outperforms the other previously mentioned denoising approaches and this based on the computation of the SNR and MSE.

scholarly journals Improved Real-time Denoising Method Based on Lifting Wavelet Transform

2014 ◽  
Vol 14 (3) ◽  
pp. 152-159 ◽  
Author(s):  
Zhaohua Liu ◽  
Yang Mi ◽  
Yuliang Mao
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Signal To Noise Ratio ◽  
Threshold Function ◽  
Time Signal ◽  
Signal Denoising ◽  
Lifting Wavelet Transform ◽  
Denoising Method ◽  
The Real ◽  
Lifting Wavelet

Abstract Signal denoising can not only enhance the signal to noise ratio (SNR) but also reduce the effect of noise. In order to satisfy the requirements of real-time signal denoising, an improved semisoft shrinkage real-time denoising method based on lifting wavelet transform was proposed. The moving data window technology realizes the real-time wavelet denoising, which employs wavelet transform based on lifting scheme to reduce computational complexity. Also hyperbolic threshold function and recursive threshold computing can ensure the dynamic characteristics of the system, in addition, it can improve the real-time calculating efficiency as well. The simulation results show that the semisoft shrinkage real-time denoising method has quite a good performance in comparison to the traditional methods, namely soft-thresholding and hard-thresholding. Therefore, this method can solve more practical engineering problems.

DWT and Lifting Wavelet Transform Based Robust Watermarking for Color Image

2015 ◽  
Vol 10 (11) ◽  
pp. 1127
Author(s):  
Nidaa Hasan Abbas ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Abed Rahman Bin Ramli ◽  
Sajida Parveen
Keyword(s):  
Wavelet Transform ◽  
Color Image ◽  
Robust Watermarking ◽  
Lifting Wavelet Transform ◽  
Lifting Wavelet

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

LWT-DCT based Image Watermarking Scheme using Normalized SVD

2020 ◽  
Vol 13 ◽  
Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Lifting Wavelet Transform ◽  
Cosine Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

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