Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet

2021 ◽  
Author(s):  
Nasser Edinne Benhassine ◽  
Abdelnour Boukaache ◽  
Djalil Boudjehem
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Wavelet Coefficients ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Selection Of

Selection of mother wavelet and decomposition level for energy management in electrical vehicles including a fuel cell

2015 ◽  
Vol 40 (45) ◽  
pp. 15823-15833 ◽  
Author(s):  
Mona Ibrahim ◽  
Samir Jemei ◽  
Geneviève Wimmer ◽  
Nadia Yousfi Steiner ◽  
Célestin C. Kokonendji ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Electrical Vehicles ◽  
Selection Of

IMAGE AND AUDIO-SPEECH DENOISING BASED ON HIGHER-ORDER STATISTICAL MODELING OF WAVELET COEFFICIENTS AND LOCAL VARIANCE ESTIMATION

2010 ◽  
Vol 08 (06) ◽  
pp. 987-1017 ◽  
Author(s):  
PICHID KITTISUWAN ◽  
THITIPORN CHANWIMALUANG ◽  
SANPARITH MARUKATAT ◽  
WIDHYAKORN ASDORNWISED
Keyword(s):  
Image Denoising ◽  
Variance Estimation ◽  
Wavelet Coefficient ◽  
Higher Order ◽  
Voice Conversion ◽  
Noise Power ◽  
Wavelet Coefficients ◽  
Denoising Method ◽  
Pearson Type ◽  
Selection Of

At first, this paper is concerned with wavelet-based image denoising using Bayesian technique. In conventional denoising process, the parameters of probability density function (PDF) are usually calculated from the first few moments, mean and variance. In the first part of our work, a new image denoising algorithm based on Pearson Type VII random vectors is proposed. This PDF is used because it allows higher-order moments to be incorporated into the noiseless wavelet coefficients' probabilistic model. One of the cruxes of the Bayesian image denoising algorithms is to estimate the variance of the clean image. Here, maximum a posterior (MAP) approach is employed for not only noiseless wavelet-coefficient estimation but also local observed variance acquisition. For the local observed variance estimation, the selection of noisy wavelet-coefficient model, either a Laplacian or a Gaussian distribution, is based upon the corrupted noise power where Gamma distribution is used as a prior for the variance. Evidently, our selection of prior is motivated by analytical and computational tractability. In our experiments, our proposed method gives promising denoising results with moderate complexity. Eventually, our image denoising method can be simply extended to audio/speech processing by forming matrix representation whose rows are formed by time segments of digital speech waveforms. This way, the use of our image denoising methods can be exploited to improve the performance of various audio/speech tasks, e.g., denoised enhancement of voice activity detection to capture voiced speech, significantly needed for speech coding and voice conversion applications. Moreover, one of the voice abnormality detections, called oropharyngeal dysphagia classification, is also required denoising method to improve the signal quality in elderly patients. We provide simple speech examples to demonstrate the prospects of our techniques.

Selection of a mother wavelet as identification pattern for the detection of cracks in shafts

2021 ◽  
pp. 107754632110260
Author(s):  
Marta Zamorano ◽  
María Jesus Gómez Garcia ◽  
Cristina Castejón
Keyword(s):  
Standard Method ◽  
Wavelet Packet ◽  
Rotation Frequency ◽  
Wavelet Function ◽  
Classification Systems ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Rotating Shaft ◽  
Critical Rotation ◽  
Selection Of

Nowadays, there are many methods to detect and diagnose defects in mechanical components during operation. The newest methods that can be found in the literature are based on intelligent classification systems and evaluation of patterns to obtain a diagnosis; however, there is not any standard method to assess features. Wavelet packet transform allows to obtain interesting patterns for evaluating the condition of rotating elements. To perform this calculation, it is necessary to select a series of parameters that affect the resulting pattern. These parameters are the decomposition level and the mother wavelet function. A detailed methodology for the selection of the mother wavelet is proposed, which is the aim of this work, to obtain the most suitable patterns in the diagnostic task. This proposed methodology is applied to data obtained from a rotating shaft with a crack located at the change of section. These signals were measured at low rotation frequency (below the critical rotation frequency) and without eccentricity, where detection becomes more complex.

scholarly journals The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1851
Author(s):  
Young In Jang ◽  
Jae Young Sim ◽  
Jong-Ryul Yang ◽  
Nam Kyu Kwon
Keyword(s):  
Wavelet Decomposition ◽  
Doppler Radar ◽  
Signal To Noise Ratio ◽  
Wavelet Function ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Snr Efficiency ◽  
Frequency Features ◽  
Selection Of

The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are “db9” and “sym9” from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field.

Medical Image Denoising With Various Noise Estimators Using Curvelet Transform

2014 ◽  
Vol 1 (2) ◽  
pp. 30-40
Author(s):  
Abha Choubey ◽  
◽  
Dr.G.R. Sinha ◽  
S. K Naik ◽  
◽  
...  
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Curvelet Transform

scholarly journals Optimal Selection of Wavelet Coefficients for Electrocardiograph Compression

ETRI Journal ◽  
2007 ◽  
Vol 29 (4) ◽  
pp. 530-532 ◽  
Author(s):  
María del Mar Elena ◽  
Jose Manuel Quero ◽  
Inmaculada Borrego
Keyword(s):  
Optimal Selection ◽  
Wavelet Coefficients ◽  
Selection Of

scholarly journals 2D–1D Wavelet Reconstruction as a Tool for Source Finding in Spectroscopic Imaging Surveys

2012 ◽  
Vol 29 (3) ◽  
pp. 244-250 ◽  
Author(s):  
L. Flöer ◽  
B. Winkel
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Spectroscopic Data ◽  
Wavelet Decomposition ◽  
Spectroscopic Imaging ◽  
Basic Method ◽  
Spectral Dimension ◽  
Wavelet Coefficients ◽  
Data Cubes ◽  
Spatial Dimensions

AbstractToday, image denoising by thresholding of wavelet coefficients is a commonly used tool for 2D image enhancement. Since the data product of spectroscopic imaging surveys has two spatial dimensions and one spectral dimension, the techniques for denoising have to be adapted to this change in dimensionality. In this paper we will review the basic method of denoising data by thresholding wavelet coefficients and implement a 2D–1D wavelet decomposition to obtain an efficient way of denoising spectroscopic data cubes. We conduct different simulations to evaluate the usefulness of the algorithm as part of a source finding pipeline.

Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

2021 ◽  
pp. 2150051
Author(s):  
S. Elavaar Kuzhali ◽  
D. S. Suresh
Keyword(s):  
Image Denoising ◽  
Image Patch ◽  
The Past ◽  
Local Means ◽  
Hybrid Optimization Algorithm ◽  
Proposed Model ◽  
Processing Step ◽  
Non Local ◽  
Chicken Swarm Optimization ◽  
Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

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