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6th International Conference on image and signal processing and their applications, ISPA 2019

作者： Seninete, Sara Mimi, Malika Eddine Cherif, Bilal Djamal Ould Ali, Abdelazziz University Abdel Hamid Ibn Badis of Mostaganem Dept. Electrical Engineering Laboratoire Signaux et Systems Mostaganem Algeria University of Science and Technology of Oran Dept. Electrical Engineering LDEE Laboratory Oran Algeria

ISBN: (纸本)9781728131566

Nowadays, fault detection in induction motors has gained importance. Motor health monitoring is performed to diagnose their operating condition using vibration signals. These signals are processed using different signal processing methods to extract the characteristic parameters permitting localization of the fault. In this paper, we propose a diagnostic method based on Hilbert and Discrete wavelet Transforms for the detection of bearing faults in asynchronous machines. The discrete wavelet transform (DWT) is intended to provide the detail coefficients while the Hilbert transform (HT) is used to obtain the temporal envelope then the envelope spectrum of the detail. The kurtosis value indicates the optimum decomposition wavelet level containing the significant frequencies corresponding to faults for early detection. The result obtained by HT-DWT is more suitable for the analysis of emergency signals. This technique is effective for either stationary or nonstationary signals. Healthy case is compared to faulty case in order to extract frequencies characterizing different faults. The validation of this approach is evaluated by comparing theoretical with experimental results. © 2019 IEEE.

关键词： Induction motors

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Deep Multi-Scale Gabor wavelet Network for image Restoration

Deep Multi-Scale Gabor Wavelet Network for Image Restoration

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Hang Dong Xinyi Zhang Yu Guo Fei Wang College of Artificial Intelligence Xi’an Jiaotong University School of Software Engineering Xi’an Jiaotong University

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Due to the limitations of the imaging processors and complex weather conditions, image degradation is often inevitable. Existing deep learning-based image restoration methods often rely on the powerful feature representation capacity of deep networks and pay less attention to the inherent properties of the degradation signal, e.g. variations in spatial scale and orientations across the image, which makes them ineffective for the image restoration tasks. In this paper, we propose a Multiscale Gabor wavelet Network (MsGWN) for image restoration. We apply the multi-scale architecture to extract the contaminated feature from input at different spatial scales, and thus the contaminated feature can be effectively restored in a corse- to-fine manner. However, using multi-scale architecture alone cannot remove the degradations with different orientations. To overcome this problem, we introduce a Gabor wavelet Module (GWM) to further extract the contaminated features from four orientations. By decomposing the features into four multi-orientation components, the restoration process can be facilitated by avoiding learning the mixed degradations all-in- one. We evaluate the proposed method on image demoirding, image deraining, and image dehazing. Experiments on these applications demonstrate that the proposed method can achieve favorable results against the state-of-the-art approaches.

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Staistical features extraction in wavelet domain for texture classification 6

Staistical features extraction in wavelet domain for texture...

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6th International Conference on image and signal processing and their applications, ISPA 2019

作者： Zehani, Soraya Ouahabi, Abdeldjalil Mimi, Malika Taleb-Ahmed, Abdelmalik Biskra University Electrical Engineering Department Biskra07000 Algeria University of Tours Polytech Tours Imaging and Brain Tours France Mostaganem University Electrical Engineering Department Mostaganem27000 Algeria UPHF IEMN DOAE UMR CNRS 8520 Valenciennes France

ISBN: (纸本)9781728131566

This paper presents a new approach for texture classification generalizing a well-known statistical features combining the fractal analysis by means of fractal dimension (FD) with the selection first and second order statistics features in the spatial and wavelet domain. The objective of our paper is to propose the features extraction using statistical parameters in the spatial domain and in wavelet domain with different wavelets, with and without preprocessing stage for the texture classification using neural networks for pattern recognition and studying the effect of the preprocessing and wavelets in classification accuracy. The extracted features are used as the input of the ANN classifier. The performance of the proposed methods are evaluated by using two classes of Brodatz database textures. Finally, classification assessment measures such as the confusion matrix, ROC curves and accuracy are applied to the proposed methods. © 2019 IEEE.

关键词： Fractal dimension

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Medical image fusion using a modified shark smell optimization algorithm and hybrid wavelet-homomorphic filter

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BIOMEDICAL signal processing AND CONTROL 2020年第0期59卷 101885-101885页

作者： Xu, Lina Si, Yujuan Jiang, Saibiao Sun, Ying Ebrahimian, Homayoun Jilin Univ Coll Instrument Sci & Elect Engn Changchun 130061 Peoples R China Jilin Univ Zhuhai Coll Zhuhai 519041 Peoples R China Univ Macau Dept Electromech Engn Taipa 999078 Macao Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China Islamic Azad Univ Ardabil Branch Ardebil Iran

Medical image fusion is a principal category in the medical applications which has great impacts on the final diagnosis results. In this study, a hybrid optimization technique is presented for developing a high efficiency technique for the fusion of the medical images. The presented method uses both advantages of the wavelet transform and the homomorphic filter for improving the system efficiency. For achieving the optimal values of the system, a new optimization algorithm based on two new introduced methods, shark smell optimization algorithm and world cup optimization algorithm is introduced. The new algorithm is then applied to the wavelet part of the system to get the optimal values. Simulations are applied on two classes of five clinical images including MR-CT, MR-SPECT, and MR-PET the results are compared with six popular methods. The final results showed that the proposed system has higher efficiency from the studied methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Multi-modal image fusion Medical image Hybrid Shark smell World Cup Optimization Algorithm Homomorphic filter wavelet transform

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Controlled Feature Adjustment for image processing and Synthesis

Controlled Feature Adjustment for Image Processing and Synth...

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IEEE Workshop on Multimedia signal processing

作者： Eduardo Martínez-Enríquez Javier Portilla Instituto de Óptica CSIC Madrid Spain

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

Feature adjustment, understood as the process aimed at modifying at will global features of given signals, has cardinal importance for several signal processing applications, such as enhancement, restoration, style transfer, and synthesis. Despite of this, it has not yet been approached from a general, theory-grounded, perspective. This work proposes a new conceptual and practical methodology that we term Controlled Feature Adjustment (CFA). CFA provides methods for, given a set of parametric global features (scalar functions of discrete signals), (1) constructing a related set of deterministically decoupled features, and (2) adjusting these new features in a controlled way, i.e., each one independently of the others. We illustrate the application of CFA by devising a spectrally-based hierarchically decoupled feature set and applying it to obtain different types of image synthesis that are not achievable using traditional (coupled) feature sets.

关键词： wavelet transforms Visualization Navigation image synthesis signal processing algorithms signal processing Aerospace electronics

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ENHANCEMENT OF A CNN-BASED DENOISER BASED ON SPATIAL AND SPECTRAL ANALYSIS 26

ENHANCEMENT OF A CNN-BASED DENOISER BASED ON SPATIAL AND SPE...

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26th IEEE International Conference on image processing (ICIP)

作者： Zhao, Rui Lam, Kin-Man Lun, Daniel P. K. Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781538662496

Convolutional neural network (CNN)-based image denoising methods have been widely studied recently, because of their high-speed processing capability and good visual quality. However, most of the existing CNN-based denoisers learn the image prior from the spatial domain, and suffer from the problem of spatially variant noise, which limits their performance in real-world image denoising tasks. In this paper, we propose a discrete wavelet denoising CNN (WDnCNN), which restores images corrupted by various noise with a single model. Since most of the content or energy of natural images resides in the low-frequency spectrum, their transformed coefficients in the frequency domain are highly imbalanced. To address this issue, we present a band normalization module (BNM) to normalize the coefficients from different parts of the frequency spectrum. Moreover, we employ a band discriminative training (BDT) criterion to enhance the model regression. We evaluate the proposed WDnCNN, and compare it with other state-of-the-art denoisers. Experimental results show that WDnCNN achieves promising performance in both synthetic and real noise reduction, making it a potential solution to many practical image denoising applications.

关键词： image denoising convolutional neural networks spatial-spectral analysis discrete wavelet transform

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Design of Parallel Pipelined Architecture for wavelet Based image Compression Using Daubechie's

Design of Parallel Pipelined Architecture for Wavelet Based ...

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2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2019

作者： Bhuvaneswary, N. Sivasankarreddy, Bandi Harshavardhanreddy, Eda Gopi, G. Kalasalingam Academy of Research and Education India

ISBN: (纸本)9781538695425

in this project, we have a tendency to be visiting exhibit VLSI design for 2D Daubechies wavelet primarily based compression. The Daubechies wavelet transform provides mean values that compress the image so it takes up a lot of less space for storing, and thus transmits quicker electronically and in progressive levels of detail. Element worth are protected by the scaling issue from prodigious their limits. To implement the system numerous levels of scaling factors given and spitted into two level. These values enforced for numerous levels of 2D-DWT. We have a tendency to store the worthiest in buffer technique and simulate the output of VLSI system. The parallel-pipelined design obtained by RTL schematic. This buffer memory design employed for input, output memory storage. This buffer memory design is employed for input output memory storage. Compared to different typical technique the performance of this design is extremely economical in compression ratio, PSNR (Peak signal to Noise Ratio), MSE (Mean sq. Error) and Time. This design is extremely economical to handle any image size. The proposed design has high compression quality as well as high speed and area-efficiency. Thisd esign is applicable for high-speed image compression and memory storage applications. © 2019 IEEE.

关键词： Discrete wavelet transforms

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Modelling curvelet based signal processing problems via wavelet analysis 23

Modelling curvelet based signal processing problems via wave...

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23rd International Congress on Modelling and Simulation - Supporting Evidence-Based Decision Making: The Role of Modelling and Simulation, MODSIM 2019

作者： Bhosale, Bharat India

ISBN: (纸本)9780975840092

Until recently, the wavelet transform has been used for Mathematical analysis and signal processing problems. But it suffers from the disadvantage of poor directionality, which has undermined its usage in many applications. The curvelet transform is a new extension of the wavelet transform, which aims to deal with intersecting phenomena occurring along curved edges in 2-D signals/images. In this work, a strong relationship between the curvelet and wavelet transforms has been established. This version of a wavelet based curvelet has been exploited to develop a full-fledged analytical framework, presenting it as an extension of well established wavelets. Due to computational complexity, instead of applying a curvelet directly, the curvelet in terms of wavelets has been employed more conveniently in the proposed signal denoising model. Finally, the performance factor analysis is performed on multispectral sample radar image data to demonstrate the efficiency of the proposed model. Besides computational gain, the proposed model shows better performance than the other signal processing models. Proposed model is equally applicable to both pulse signals and digital images. Copyright © 2019 The Modelling and Simulation Society of Australia and New Zealand Inc. All rights reserved.

关键词： wavelet transforms

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VLSI Architecture Design of 9/7 Discrete wavelet Transform for image processing 62

VLSI Architecture Design of 9/7 Discrete Wavelet Transform f...

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62nd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Javed, Sadaf Younis, Ch. Jabbar Alam, Mehboob Massoud, Yehia Mirpur Univ Sci & Technol Dept Elect Engn Mirpur 10250 Ajk Pakistan Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

ISBN: (纸本)9781728127880

In image processing, transform coding de-correlates images to pre-condition them for efficient compression. In this work, we propose VLSI architecture design of a hardware-efficient 9/7 Discrete wavelet Transform (DWT). The architecture takes advantage of Canonical Signed Digit (CSD) and Distributed Arithmetic (DA) to represent and optimally distribute co-efficients to reduce the number of adder and shift registers. In addition, the co-efficient multiplication also exploits the horizontal and vertical redundancy in the architecture to reduce the hardware computational complexity. The result is a filter-based design, exploiting hardware path of architecture using CSD coefficients, which finds minimum realization. The proposed architecture is simulated using Verilog Hardware Description Language (HDL). A comparison with other architectures of 9/7 DWT shows a 18.75% reduction in hardware. The result is a hardware-efficient architecture, which provides a low-power solution for image and signal processing applications.

关键词： Discrete wavelet Transform VLSI Distributed Arithmetic image processing Canonical Signed Digit

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Vascular Auscultation of Carotid Artery: Towards Biometric Identification and Verification of Individuals

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SENSORS 2021年第19期21卷 6656-6656页

作者： Salvi, Rutuja Fuentealba, Patricio Henze, Jasmin Bisgin, Pinar Suehn, Thomas Spiller, Moritz Burmann, Anja Boese, Axel Illanes, Alfredo Friebe, Michael IDTM GmbH Ingn Gesell Diagnost & Therapeut Med Te D-45657 Recklinghausen Germany Univ Austral Chile Fac Ciencias Ingn Inst Elect & Elect Valdivia 5111187 Chile Fraunhofer Inst Software & Syst Engn D-44227 Dortmund Germany Otto von Guericke Univ INKA Innovat Lab Image Guided Therapy D-39120 Magdeburg Germany SURAG Med GmbH Surg Audio Guidance D-39120 Magdeburg Germany MED GmbH Med Innovat Certificat Serv D-39114 Magdeburg Germany

Background: Biometric sensing is a security method for protecting information and property. State-of-the-art biometric traits are behavioral and physiological in nature. However, they are vulnerable to tampering and forgery. Methods: The proposed approach uses blood flow sounds in the carotid artery as a source of biometric information. A handheld sensing device and an associated desktop application were built. Between 80 and 160 carotid recordings of 11 s in length were acquired from seven individuals each. wavelet-based signal analysis was performed to assess the potential for biometric applications. Results: The acquired signals per individual proved to be consistent within one carotid sound recording and between multiple recordings spaced by several weeks. The averaged continuous wavelet transform spectra for all cardiac cycles of one recording showed specific spectral characteristics in the time-frequency domain, allowing for the discrimination of individuals, which could potentially serve as an individual fingerprint of the carotid sound. This is also supported by the quantitative analysis consisting of a small convolutional neural network, which was able to differentiate between different users with over 95% accuracy. Conclusion: The proposed approach and processing pipeline appeared promising for the discrimination of individuals. The biometrical recognition could clinically be used to obtain and highlight differences from a previously established personalized audio profile and subsequently could provide information on the source of the deviation as well as on its effects on the individual's health. The limited number of individuals and recordings require a study in a larger population along with an investigation of the long-term spectral stability of carotid sounds to assess its potential as a biometric marker. Nevertheless, the approach opens the perspective for automatic feature extraction and classification.

关键词： authentication biometric vascular sounds carotid artery spoofing

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