scholarly journals PLASMA STRUCTURE EXTRACTION FROM LASCO IMAGES BY THE DUAL-TREE COMPLEX WAVELET TRANSFORM

2015 ◽  
Vol 33 (1) ◽  
pp. 45
Author(s):  
Vitor Moura Souza ◽  
Margarete Oliveira Domingues ◽  
Odim Mendes ◽  
Aylton Pagamisse
Keyword(s):  
Wavelet Transform ◽  
Large Angle ◽  
Magnetized Plasma ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Computationally Efficient ◽  
Geomagnetic Disturbances ◽  
Solar Plasma ◽  
Complex Wavelet ◽  
Computationally Efficient Algorithms

ABSTRACT. Images of the Sun obtained in distinct electromagnetic ranges can be very useful to investigate electrodynamical processes that occur in this star. This work analyzes the application of a two-dimensional version of the Dual-Tree Complex Wavelet Transform (DTCWT) to highlight physical features in the Large Angle and Spectrometric Coronograph images. The importance of those features is that they are related with coronal ejections of magnetized solar plasma, which can produce geomagnetic disturbances at the Earth. The DTCWT is a laborious improvement of the well-known Discrete Wavelet Transform (DWT) with additional properties of shift invariance, ability to analyze multiple directions for multidimensional signals, and computationally efficient algorithms. The multilevel decomposition of an image with the DTCWT generates complex wavelet coefficients, which are manipulated for providing a proper visualization of the plasma structures, highlighting features, and helping further analyses. The methodology implemented here is of interest to space weather laboratories and has been shown to be a very useful tool to a better identification and characterization of the features related to magnetized plasma phenomena in the solar corona.Keywords: coronal mass ejection, solar plasma, solar corona images, complex wavelet transform, space weather.RESUMO. Imagens do Sol obtidas em diferentes intervalos do espectro eletromagnético podem ser bem úteis na investigação de processos eletrodinâmicos que ocorrem nesta estrela. Este trabalho analisa a aplicação de uma versão bidimensional da Transformada Wavelet Complexa de dupla árvore (DTCWT) para destacar características físicas nas imagens do Coronógrafo Espectométrico de Ângulo Largo (LASCO). A importância dessas características é que elas estão relacionadas a ejeções coronais de plasma solar magnetizado, que podem produzir perturbações geomagnéticas na Terra. A DTCWT é um aprimoramento laborioso da bem conhecida Transformada Wavelet Discreta (DWT), com propriedades adicionais de: (i) invariância a deslocamentos, (ii) habilidade em analisar múltiplas direções para sinais multidimensionais, e (iii) algoritmo computacionalmente eficiente. A decomposição multinível de uma imagem com a DTCWT gera coeficientes wavelets complexos, que são manipulados para prover uma visualização adequada das estruturas de plasmas, destacando características, e ajudando nas análises posteriores. De interesse dos laboratórios de Clima Espacial, a metodologia implementada aqui mostrou-se ser muito útil para uma melhor identificação e caracterização dos fenômenos de plasmas magnetizados na coroa solar.Palavras-chave: ejeção de massa coronal, plasma solar, imagens da coroa solar, transformada wavelet complexa, clima espacial.

Daubechies Complex Wavelet-Based Computer Vision Applications

2013 ◽  
pp. 138-155 ◽  
Author(s):  
Manish Khare ◽  
Rajneesh Kumar Srivastava ◽  
Ashish Khare
Keyword(s):  
Computer Vision ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Daubechies Complex Wavelet Transform ◽  
Computer Vision Applications ◽  
Complex Wavelet ◽  
Almost All ◽  
Moving Shadow Detection

Many methods for computer vision applications have been developed using wavelet theory. Almost all of them are based on real-valued discrete wavelet transform. This chapter introduces two computer vision applications, namely moving object segmentation and moving shadow detection and removal, using Daubechies complex wavelet transform. Daubechies complex wavelet transform has advantages over discrete wavelet transform as it is approximately shift-invariant, has a better edge detection, and provides true phase information. Results after applying Daubechies complex wavelet transform on these two applications demonstrate that Daubechies complex wavelet transform-based methods provide better results than other real-valued wavelet transform-based methods, and it also demonstrates that Daubechies complex wavelet transform has the potential to be applied to other computer vision applications.

scholarly journals Dual tree complex wavelet transform-based signal denoising method exploiting neighbourhood dependencies and goodness-of-fit test

2018 ◽  
Vol 5 (9) ◽  
pp. 180436 ◽  
Author(s):  
Khuram Naveed ◽  
Bisma Shaukat ◽  
Naveed ur Rehman
Keyword(s):  
Wavelet Transform ◽  
Goodness Of Fit ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Complex Wavelet Transform ◽  
Goodness Of Fit Test ◽  
Denoising Method ◽  
Complex Wavelet

A novel signal denoising method is proposed whereby goodness-of-fit (GOF) test in combination with a majority classifications-based neighbourhood filtering is employed on complex wavelet coefficients obtained by applying dual tree complex wavelet transform (DT-CWT) on a noisy signal. The DT-CWT has proven to be a better tool for signal denoising as compared to the conventional discrete wavelet transform (DWT) owing to its approximate translation invariance. The proposed framework exploits statistical neighbourhood dependencies by performing the GOF test locally on the DT-CWT coefficients for their preliminary classification/detection as signal or noise. Next, a deterministic neighbourhood filtering approach based on majority noise classifications is employed to detect false classification of signal coefficients as noise (via the GOF test) which are subsequently restored. The proposed method shows competitive performance against the state of the art in signal denoising.

scholarly journals FORMATION OF WAVELET - TRANSFORM SIGNALS TAKING INTO ACCOUNT THRESHOLD FUNCTIONS IN THE DIGITAL STREAM COMPRESSION PROBLEM

2020 ◽  
pp. 186-197
Author(s):  
Valentina Solodka ◽  
Olena Osharovska ◽  
Mukola Patlayenko ◽  
Anastasia Podolska
Keyword(s):  
Wavelet Transform ◽  
Threshold Function ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Complex Wavelet Transform ◽  
Threshold Functions ◽  
Useful Signal ◽  
Mandatory Requirement ◽  
Complex Wavelet ◽  
Amplitude Characteristics

In order to reduce the digital stream, the problem of improving the quality of digital filtering based on Discrete Wavelet Transformation (DWT) was analyzed using different options for setting the threshold function and the choice of wavelet basis. Given the considerable interest in this problem and the numerous studies concerning the search for ways to optimize the suppression of interference present in signals and images, the topic continues to be relevant and important. For such tasks as image compression, operations in processing and synthesis of different signals, in the analysis of images of different nature, in reducing (compressing) large amounts of information, and to protect information is often used wavelet transform, implemented on the basis of mirror filters. For the formation of wavelet-transform signals taking into account the threshold functions in the problem of digital stream compression, a standard approach to solving the problem of signal purification from interference and random distortions using Daubechies wavelet and adjusting the signal decomposition coefficients based on wavelet functions using soft and rigid threshold task options. It is shown that the use of complex bases provides an advantage both in terms of threshold filtering error and in terms of reducing the risk of accidental distortion in the reconstruction of the useful signal by wavelet coefficients. Appropriate steps were taken for the test signal and experimental data. When using the threshold function, the large modulus (most significant) wavelet coefficients remain unchanged, and the small ones are reset to zero. The change in the amplitude of the recovered signal leads in the latter case to a decrease in the absolute values of all wavelet coefficients, including large modulo. For those applications where it is important to keep the amplitude characteristics constant, this approach is not suitable, but there are problems where it is more important to maintain the regularity of the signal than to accurately reproduce its amplitude. Rhis is the filtering of images from various obstacles, where the method of "soft" task of the threshold function is a widely used approach. When analyzing signals, the constant amplitude is also not always a mandatory requirement. An audio signal can be amplified after filtering, and pre-cleaning it from interference is more important than changing the amplitude characteristics. The use of complex wavelet transform allowed to obtain error reduction and lower threshold level when adjusting wavelet coefficients, and can be recommended for analysis of complex wavelet transform methods as an effective tool for cleaning signals and images of different nature for further research.

scholarly journals Perbandingan Real-Valued dan Complex Wavelet Transform pada Denoising Sinyal Fetal-Phonocardiograms (Comparison of Fetal-Phonocardiogram Signal Denoising based on Real- Valued and Complex Wavelet Transform)

2020 ◽  
Vol 9 (1) ◽  
pp. 63-72
Author(s):  
Dodi Zulherman ◽  
Jans Hendry ◽  
Ipam Fuadina Adam
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Well Being ◽  
Discrete Wavelet ◽  
Interference Signal ◽  
Complex Wavelet Transform ◽  
Invasive Method ◽  
Complex Wavelet ◽  
Acoustic Recording

Monitoring of Fetal Heart Rate (FHR) in the pregnancy period commonly uses the Doppler-based instruments despite having several disadvantages, such as high-cost and complexity of the monitoring system. Implementation of the passive and non-invasive method based on fetal phonocardiogram (fPCG), the acoustic recording of fetus cardiac signal, can be used as a potentially economical long-term monitoring device for diagnosis. Because the interference signal from the maternal women exists, the matured denoising technique was needed to implement the fPCG method to diagnose the fetus' well-being condition. The denoising system based on Dual-tree Complex Wavelet Transforms (DTCWT) was proposed in this paper. The proposed method was evaluated using Signal to Noise Ratio (SNR). Based on the experiment result from 37 fPCG signals from physio.net, the DTCWT system performance was compared with the Discrete Wavelet Transform (DWT). There were 24 CWT’s denoised fPCG signals that have successfully outperformed DWT’s SNR. DTCWT has also reduced the noises in the range of 30 Hz–80 Hz. Also, it emphasized the existence of dominant frequencies in the range of 60 Hz–65 Hz.

scholarly journals Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Hermanus Vermaak ◽  
Philibert Nsengiyumva ◽  
Nicolaas Luwes
Keyword(s):  
Wavelet Transform ◽  
Defect Detection ◽  
Texture Classification ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Undecimated Wavelet Transform ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Complex Wavelet ◽  
Undecimated Wavelet

The dual-tree complex wavelet transform (DTCWT) solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT). It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG), are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT) with a detection rate of 4.5% to 15.8% higher depending on the fabric type.

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