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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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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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Denoising of Single-Trial Event-Related Potentials by Shrinkage and Phase Regularization of Analytic wavelet Filterbank Coefficients 9

Denoising of Single-Trial Event-Related Potentials by Shrink...

引用

9th IEEE/EMBS International conference on Neural Engineering (NER)

作者： Kohl, Manuel C. Schebsdat, Erik Schneider, Elena N. Strauss, Daniel J. Saarland Univ Hosp Neuroctr Syst Neurosci & Neurotechnol Unit Homburg Germany Saarland Univ Appl Sci Saarbrucken Germany Key Numer GmbH Saarbrucken Germany

ISBN: (纸本)9781538679210

Event-related potentials (ERP) provide reliable electrophysiological correlates of subsequent neural processing following sensory stimulation, offering insight into the activation patterns of participating neural structures which is of considerable value in both neuroscience research and clinical applications. 2D single-trial representations as ERP images have seen increased application in recent studies, accompanied by a rising number of approaches to improve their signal-to-noise ratio, which for the most part have been motivated from an image processing point of view (e.g., nonlocal operators, anisotropic diffusion filtering). In this paper, a brief overview of ERP image denoising prior art is given and a novel, fast denoising algorithm based on split amplitude and phase processing (i.e., phase-informed amplitude shrinkage and regularization of the phase structure) in analytic time-frequency representations of ERP single trials obtained using a perfect reconstruction wavelet filterbank is proposed. Furthermore, the performance of the proposed algorithm is subjected to a comparative evaluation using real-world chirp-evoked auditory ERP acquired from 20 normal hearing adults. Results suggest the suitability of the proposed method for a broad range of a posteriori ERP image denoising tasks, including those lacking a priori knowledge about the shape of potentially nonstationary traces in the ERP image due to, e.g., endogeneous states gradually changing during the experiment.

关键词： Noise reduction Transforms image denoising Chirp Measurement Auditory system signal to noise ratio

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Automated Hyperspectral image Classification Using Spatial-Spectral Features 6

Automated Hyperspectral Image Classification Using Spatial-S...

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6th International conference on signal processing and Integrated Networks (SPIN)

作者： Dhok, Shivani Bhurane, Ankit Kothari, Ashwin Nagpur IIITN Indian Inst Informat Technol Dept Elect & Commun Engn Nagpur Maharashtra India Visvesvaraya Natl Inst Technol Dept Elect & Commun Engn Nagpur Maharashtra India

ISBN: (纸本)9781728113807

Hyperspectral imaging has transpired as a compelling tool in various fields like geology, milling, agriculture, etc with applications ranging from object detection to quality inspection. Feature extraction, as well as the methodology used for feature extraction, plays an indispensable role in increasing the accuracy of the classification of hyperspectral imaging (HSI). This paper proposes an algorithm for automated hyperspectral image classification using nine spatial-spectral features, which includes linear predictive coefficients, wavelet coefficients, standard deviation, average energy, mean, fractal dimension, entropy, Renyi entropy and Kraskov entropy. These features are further used for classification using the quadratic support vector machine (SVM). The elaborated scheme exercises 10-fold cross-validation. The collective effect of the excerpted features is determined and the accuracy trends for the various number of features is ascertained. Appreciable overall accuracies (OA) for all the three publicly available data sets are acquired as follows: Salinas-A data set (OA = 99.60%), Salinas data set (OA = 92.4%) and Botswana data set (OA = 89.5%).

关键词： hyperspectral imaging linear predictive coefficients wavelet coefficients Kraskov entropy Renyi entropy fractal dimension

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Multidimensional images processing in electrocardiography using Bragg diffraction technologies 9

Multidimensional images processing in electrocardiography us...

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conference on Medical Laser applications and Laser-Tissue Interactions IX

作者： Zaichenko, K. V. Gurevich, B. S. RAS Inst Analyt Instrumentat 26 Rizhsky Pr St Petersburg 190103 Russia State Univ Aerosp Instrumentat Bolshaya Morskaya Str 67 St Petersburg 90000 Russia St Petersburg Electrotech Univ 5 Prof Popova St Petersburg 197376 Russia

ISBN: (数字)9781510628526

ISBN: (纸本)9781510628526

Multidimensional images are often used for the most convenient representation of the processes carrying out in the studied object. However, sometimes, especially in such areas as medical diagnostics or related ones, it is required to estimate quickly simultaneously several aspects of the observed process, and certain part of multidimensional image must be observed in details without influence of several excessive signal distribution along some dimensions. The acousto-optic method based on Bragg diffraction has been proposed for this problem solution which provides fast and automatic (controlled by software) way of multidimensional switching to its selective part or several parts. Especially this method is convenient in electrocardiography where signal processing produces such pictures as 3D mapping images or signal wavelet transform images. Information losses which appear due to this method application, have been estimated, and the ways of the method optimization from the point of view of maximum signal-to-noise ratio providing, have been proposed.

关键词： Bragg diffraction acousto-optic device electrocardiography 3D mapping information losses bioelectric signals wavelet processing signal-to-noise ratio

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Proceedings of the 2019 IEEE International conference on Communication and signal processing, ICCSP 2019

Proceedings of the 2019 IEEE International Conference on Com...

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8th IEEE International conference on Communication and signal processing, ICCSP 2019

ISBN: (纸本)9781538675953

The proceedings contain 206 papers. The topics discussed include: wavelet based analysis of EEG signal for detecting various conditions of driver;cluster based reliable communication for 5G network;grab force measurement for hand orthosis;design of millimeter wave LC oscillators for 5G applications;a review on multi-model medical image fusion;pattern generation and dimensionality reduction using CNN for image restoration;driver face recognition and sober drunk classification using thermal images;transfer learning with ResNet-50 for malaria cell-image classification;implementation of deep learning algorithm with perceptron using TenzorFlow library;a comparative analysis of machine learning algorithms in detecting deceptive behavior in humans using thermal images;and image segmentation based on Markov random field probabilistic approach.

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Auto Center Find Density Peaks Algorithm and its Application in Face image Clustering

Auto Center Find Density Peaks Algorithm and its Application...

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2019 IEEE International conference on signal, Information and Data processing, ICSIDP 2019

作者： Wang, Guiyan Bu, Changjiang Luo, Yuesheng College of Automation Harbin Engineering University Harbin China Harbin Finance University Department of Basic Research Harbin China

ISBN: (纸本)9781728123455

Face image clustering is one of the most important applications in digital signal process. Here, Find Density Peaks clustering algorithm with auto choosing centers is proposed and applied into the face image clustering problem. In the clustering process, the Complex wavelet structural Similarity Index is introduced to quantize the difference between two face images. The deciding mechanism of two key parameters are modified. Avoiding the false centers, the true centers of each cluster are auto defined. And the face images dataset are properly clustered. The face images are dimensionality reduced and visualized via T-SNE as scatter points in 2D space. The results show a good partition of face images dataset. And the proposed algorithm shows practical values in most applications required with clustering. © 2019 IEEE.

关键词： Clustering algorithms

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Super Resolution of DS-2 Satellite imagery using Deep Convolutional Neural Network 25

Super Resolution of DS-2 Satellite Imagery using Deep Convol...

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conference on image and signal processing for Remote Sensing XXV

作者： Aburaed, Nour Panthakkan, Alavi Almansoori, Saeed Al-Ahmad, Hussain Univ Dubai Dubai U Arab Emirates Mohammed bin Rashid Space Ctr Dubai U Arab Emirates

ISBN: (数字)9781510630147

ISBN: (纸本)9781510630147

Nowadays, Satellite images are used for various analysis, including building detection and road extraction, which are directly beneficial to governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. High spatial resolution means satellite images provide more detailed information. To improve the spatial resolution at the sensor level, many factors are ought to be taken into consideration, such as the manufacturing process. Moreover, once the satellite is launched, no further action can be taken from the perspective of hardware. Therefore, a more practical solution to improve the resolution of a satellite image is to use Single image Super Resolution (SISR) techniques. This research proposal deals with the re-design, implementation, and evaluation of SISR technique using Deep Convolutional Neural Network with Skip Connections and Network in Network (DCSCN) for enlarging multispectral remote sensing images captured by DubaiSat-2 (DS-2) and estimating the missing high frequency details. The goal is to achieve high performance in terms of quality, and to test whether training the network using luminance channel only, which is extracted from YCbCr domain, can achieve high quality results. For this purpose, DCSCN is trained, evaluated, and tested using a dataset collected from DS-2. A single low resolution DS-2 image is used to construct its high resolution version by training the model from scratch and fine-tuning its hyper-parameters to produce optimal results. The performance is evaluated using various quality indices, such as Structural Similarity Index Measurement (SSIM), Peak signal-to-Noise Ratio (PSNR), and wavelet domain signal-to-Noise Ratio (WSNR). The performance is compared to other state-of-the-art methods, such as Bil-inear, Bi-cubic, and Lanczos interpolation.

关键词： image Reconstruction Super Resolution Deep Learning Multispectral Peak signal to Noise Ratio (PSNR) wavelet domain signal to Noise Ratio (WSNR) Structure Similarity Index Measurement (SSIM)

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DWT-QR based blind image watermarking method using firefly algorithm

DWT-QR based blind image watermarking method using firefly a...

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IEEE signal processing and Communications applications (SIU)

作者： Şeyma Yücel Altay Güzin Ulutaş Atatürk Üniversitesi Erzurum Türkiye Karadeniz Teknik Üniversitesi Trabzon Türkiye

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

Digital watermarking is a method used to protect intellectual property rights and copyrights. This study represents a blind method based on Discrete wavelet Transform (DWT) and QR decomposition. In this method, the cover image is firstly divided into non-overlapping blocks, and then the blocks with low standard deviation are selected to embed the watermark. After each of the selected blocks is decomposed by DWT, low frequency sub-bands are subjected to QR decomposition. The first row of the R matrix resulting from QR decomposition is used to embed the watermark bits. Firefly Algorithm (FA) is utilized to optimize the gain factor of embedding, which is effective in determining the robustness and imperceptibility of the watermarking scheme. To appraise the performance of the study, Peak signal to Noise Ratio (PSNR) is used to measure the perceptual quality and Normalized Correlation (NC) is used to evaluate robustness. According to experimental results, the perceptual quality of the proposed method is found to be higher than that of similar studies in the literature. In addition, it is established that the robustness of the method against attacks such as noise, cropping, compression, and filtering is better.

关键词： Watermarking Discrete wavelet transforms Blogs PSNR Robustness Transforms Correlation

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A Hybrid Multi-focus image Fusion Technique using SWT and PCA

A Hybrid Multi-focus Image Fusion Technique using SWT and PC...

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International conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Tushar Tyagi Parth Gupta Prabhishek Singh Department of CSE Amity University Uttar Pradesh Noida India

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. It is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal image Quality Index), and PSNR (Peak signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).

关键词： conferences Cloud computing Data science Principal component analysis wavelet analysis wavelet transforms

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