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Conference on Holography, Diffractive Optics, and applications viii

作者： Rajput, Sudheesh K. Matoba, Osamu Kobe Univ Grad Sch Syst Informat Nada Ku Rokkodai 1-1 Kobe Hyogo 6578501 Japan

ISBN: (数字)9781510622357

ISBN: (纸本)9781510622357

Digital holography based sound propagation imaging works on phase change characteristics of a medium where sound wave propagates. Spatial and temporal distribution of sound field can be obtained by measuring phase modulation of light caused by sound field. In this technique, object wave passes near to vibrating object and the interference patterns are recorded in digital holograms as a function of time. After using numerical reconstruction by angular spectrum method and acousto-optic data processing, sound field can be visualized. This technique has been tested for vibration frequency measurement and can also visualize the human voice by optical means. It has also been used for optical voice encryption, which can secure voice. For vibration frequency measurement, Fourier analysis is used and for voice encryption, optical image encryption techniques have been utilized. In this paper, we present our proposed sound wave imaging scheme and its applications in frequency estimation and voice encryption. In the support of sound imaging scheme and its applications, we present detailed theory and experimental results.

关键词： Digital holography Holographic interferometry Acousto-optical signal processing

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Implementation of Lifting Scheme Discrete wavelet Transform Using Modified Multiplier 1st

Implementation of Lifting Scheme Discrete Wavelet Transform ...

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1st International Conference on Advanced Computational and Communication Paradigms (ICACCP)

作者： Kumar, G. Kishore Balaji, N. VRSEC Dept ECE Vijayawada 520007 Andhra Pradesh India JNTUK UCEN Dept ECE Narasaraopet 522603 Andhra Pradesh India

ISBN: (纸本)9789811082405;9789811082399

Performance of multipliers has a tremendous impact on system-level functionality especially in signal processing and image processing applications. In this short, the sum-of-power-of-two (SOPOT)-based multiplier is investigated and from this investigation some unnecessary shifters and multiplexers are observed. The unnecessary shifters and multiplexers exist in SOPOT architecture are eliminated and a novel multiplier is proposed using canonical-signed digit (CSD) representation and also the size of control register is reduced. The modified architecture is superior to the existing architecture as there is a reduction in area and power consumption. The lifting scheme discrete wavelet transform has been implemented using modified multiplier. The synthesis results show the modified CSD architecture area delay product (ADP) and energy are less than the previous design respectively. © Springer Nature Singapore Pte Ltd. 2018.

关键词： ASIC CSD Discrete wavelet transform SOPOT

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Neural network based robust image watermarking technique in LWT domain

Neural network based robust image watermarking technique in ...

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3rd International Symposium on Intelligent Systems Technologies and applications (ISTA)

作者： Islam, Mohiul Roy, Amarjit Laskar, Rabul Hussain Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar 788010 Assam India BML Munjal Univ Sch Engn & Technol Gurgaon Haryana India

In this paper, a robust image watermarking technique has been proposed in lifting wavelet transform (LWT) domain. Neural network is incorporated in the watermark extraction process to achieve improved robustness against different attacks. The integration of neural network with LWT makes the system robust to various attacks maintaining an adequate level of imperceptibility. The 3-level LWT coefficients are randomized and arranged in 2x2 non-overlapping blocks. Each block is modified according to a binary watermark bit. Randomization of coefficients and blocks has been done to enhance the security of the system. The binary watermark bit is also encrypted using another key. The scheme provides an average imperceptibility of 43.88 dB for a watermark capacity of 512 bits. The robustness has been observed against all the intentional and non-intentional attacks. The technique provides satisfactory robustness against different attacks such as noising attacks, de-noising attacks, lossy compression attacks, image processing attacks and some geometric attacks. The algorithm has been tested on a large image database containing different class of images.

关键词： Lifting wavelet transform (LWT) image watermarking artificial neural network (ANN) peak signal to noise ratio (PSNR) normalized cross-correlation (NC)

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Lenslet Light Field Imaging Scalable Coding 26

Lenslet Light Field Imaging Scalable Coding

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26th European signal processing Conference (EUSIPCO)

作者： Garrote, Joao Brites, Catarina Ascenso, Joao Pereira, Fernando Univ Lisbon Inst Telecomunicacoes Inst Super Tecn Av Rovisco Pais P-1049001 Lisbon Portugal

ISBN: (纸本)9789082797015

Light fields have emerged as one of the most promising 3D representation formats, enabling a richer and more immersive representation of a visual scene. The lenslet light field acquisition approach consists in placing an array of micro-lenses between the camera main lens and the photosensor to allow capturing both the intensity and the direction of the light rays. This type of representation format offers new interaction possibilities with the visual content, notably a posteriori refocusing and visualization of different perspectives of the visual scene. However, this representation model is associated to very large amounts of data, thus requiring efficient coding solutions in order applications involving storage and transmission may be deployed. This paper proposes a novel lenslet light field imaging scalable coding solution adopting a wavelet-based approach, able to offer view, quality and spatial scalabilities, to meet the characteristics of multiple types of displays, transmission channels and user needs. The performance results show that the proposed coding solution performs better than alternative scalable coding solutions, notably JPEG 2000.

关键词： lenslet light field sub-aperture image disparity estimation and compensation scalability JPEG 2000

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A study of wavelet-based denoising and a new shrinkage function for low-dose CT scans

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BIOMEDICAL PHYSICS & ENGINEERING EXPRESS 2019年第3期5卷 035018-035018页

作者： Mohammadi, Sadegh Leventouri, Th Florida Atlantic Univ Dept Phys Boca Raton FL 33431 USA

wavelet transformation is known as a strong method for signal and image processing tasks in the medical applications. We utilized this transformation to reduce the noise in low-dose x-ray computed tomography (CT) images. The CT images had been acquired with 75% less radiation dose compared to the normal dose scans. In this paper we propose a shrinkage function that outperforms the traditional ones and it doesn't require any selected parameters, if it is tuned for the specific region of the patient volume. In addition, the denoising performances of combinations of wavelet orders, decomposition levels, and thresholding methods were investigated. The results revealed the best combination of wavelet order and decomposition level for low dose CT denoising.

关键词： image denoising low-dose CT medical diagnosis noise reduction wavelet transforms

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Statistical analysis of fMRI using wavelets: Big Data, denoising, large-p-small-n matrices

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2019年第5期11卷 e1467-e1467页

作者： Efromovich, Sam Univ Texas Dallas Dept Math Sci Richardson TX 75080 USA

Over the past decade functional Magnetic Resonance Imaging (fMRI) has been intensively used to study the complex functional network organization of the human brain and how it changes in time. An fMRI machine produces 3D time-course cerebral images that contain hundreds of thousands of voxels and each voxel is scanned for hundreds of times. This potentially allows the researchers to explore functional connectivity on a voxel-to-voxel level, and also yields a number of serious statistical complications. First of all, the high-dimension property of fMRI data turns it into Big Data. Furthermore, the study of functional brain network for so many voxels involves the problem of estimation of and simultaneous inference for large-p-small-n cross-covariance matrices. Furthermore, all problems should be solved in the presence of notoriously large fMRI noise which often forces statisticians to average signals over large areas instead of considering a network between individual voxels. An attractive alternative to the averaging, discussed in the paper, is a multiresolution wavelet analysis complemented by special procedures of estimating noise and estimation and simultaneous inference for cross-covariance and cross-correlation matrices for hundreds of thousands pairs of voxels, and it is fair to say that if wavelets have not been already known, fMRI applications would necessitates their creation. Both task and resting-state fMRI are considered, and lessons from the wavelet analysis of ultra-fast and conventional neuroplasticity fMRI experiments are presented. The article is self-contained and does not require familiarity with wavelets or fMRI. This article is categorized under: Algorithms and Computational Methods > Numerical Methods applications of Computational Statistics > signal and image processing and Coding

关键词： Big Data fMRI wavelet analysis

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Multiscale LMMSE-Based Statistical Estimation for image Denoising 1st

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1st International Conference on Soft Computing: Theories and applications, SoCTA 2016

作者： Gupta, Rajesh Kumar Anand, Milind Bai, Anita Department of Computer Science & Engineering National Institute of Technology Rourkela India Department of Computer Science & Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (纸本)9789811056864

Denoising of a legitimate image depraved by the additive white Gaussian noise (AWGN) is a famous problem in image processing. Thresholding does the extraction from noisy wavelet coefficients using denoising method by reserving the major coefficients and adjusting the remaining to zero. In this paper, extraction of Gaussian noise from vociferous image is done. The proposed scheme in this paper outmatches some present denoising method by using linear minimum mean square error (LMMSE) based maximum aposteriori (MAP) estimation. Some parameters are altered to alienatet the noise proficiently, such as variance of the classical MMSE estimator of the noisy wavelet coefficients in the neighborhood window. Each and every procedure is analyzed separately and experiments are operated to evaluate their performance in view of peak signal-to-noise ratio (PSNR). Improved results are obtained for highly corrupted inartificial images. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Discrete wavelet transforms

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image Compression using 2D-Discrete wavelet Transform on a Light-Weight Reconfigurable Hardware 31

Image Compression using 2D-Discrete Wavelet Transform on a L...

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31st International Conference on VLSI Design / 17th International Conference on Embedded Systems (VLSID & ES))

作者： Jain, Nupur Singh, Mansi Mishra, Biswajit Dhirubhai Ambani Inst Informat & Commun Technol VLSI Gandhinagar 382007 Gujarat India Dhirubhai Ambani Inst Informat & Commun Technol Embedded Syst Res Grp Gandhinagar 382007 Gujarat India

ISBN: (纸本)9781538636923

This paper presents a hardware implementation of a 2D-DWT on a reconfigurable architecture targeting image processing applications. The architecture is capable of performing various other digital signal and image processing functions such as CORDIC, FIR filtering, 2D convolution and DCT to compute transforms, trigonometric functions etc. In this paper, we present the mapping of a configurable 2D-DWT algorithm using convolution method with separable filter approach having filter length upto 8 taps on the reconfigurable architecture hardware. The reconfigurable hardware architecture mapped with 2D-DWT is ported onto an FPGA that has a frequency of operation of 37.26 MHz. For a 1-level decomposition, the number of clock cycles are 496 per 8x8 block of the NxN image with a total clock cycles equals 31N(2)/4 with 75% compression and can be further improved by computing higher levels of decomposition of the 2D-DWT.

关键词： 2D-DWT image processing Reconfigurable Hardware wavelets

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Brillouin optical time domain analyzer sensors assisted by advanced image denoising techniques

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OPTICS EXPRESS 2018年第5期26卷 5126-5139页

作者： Wu, Huan Wang, Liang Zhao, Zhiyong Guo, Nan Shu, Chester Lu, Chao Chinese Univ Hong Kong Dept Elect Engn Sha Tin Hong Kong Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Kowloon Hong Kong Peoples R China

We have experimentally analyzed and compared the performance of Brillouin optical time-domain analyzer (BOTDA) sensors assisted by non-local means (NLM) and wavelet denoising (WD) techniques in terms of measurement accuracy and experimental spatial resolution, respectively. Degradation of the measurement accuracy and experimental spatial resolution after denoising by NLM and WD are observed, which originate from the fact that higher signal-to-noise ratio (SNR) improvement is achieved at the expense of sacrificing the details of BOTDA data, and smaller data sampling point number (SPN) gives rise to insufficient redundant information for denoising. The two parameters degrade to different extents depending on the amount of SNR improvement and SPN adopted in data acquisition. Compared with WD, NLM relies more on the features of the raw data, which makes its performance highly dependent on the level of neighbouring data similarity. Also, for the first time we propose and demonstrate a BOTDA assisted by advanced BlockMatching and 3D filtering (BM3D) denoising technique, which minimizes the degradation of the two parameters even under higher SNR improvement and smaller SPN. BM3D takes the advantage of NLM and WD and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain, thus it shows the least degradation of measurement accuracy/experimental spatial resolution after denoising. Thus the BOTDA assisted by BM3D maintains the best measurement accuracy/experimental spatial resolution compared with those by NLM and WD. We also show that BM3D has the advantage of temperature independent performance, unlike NLM where the accuracy is affected by the temperature value. We believe BM3D would be an excellent denoising technique for state-of-the-art BOTDA sensors. In addition, this work is also valuable for practical applications of image denoising techniques in BOTDA sensors with respect to the appropriate choice of image denoising techniques

关键词： Absolute distance measurement Brillouin scattering Fiber optic sensors image processing Spatial resolution wavelet transforms

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Graph signal processing for visual analysis and data exploration

Graph signal processing for visual analysis and data explora...

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作者： Valdivia, Paola Tatiana Llerena Universidade de Lisboa

signal processing is used in a wide variety of applications, ranging from digital image processing to biomedicine. Recently, some tools from signal processing have been extended to the context of graphs, allowing its use on irregular domains. Among others, the Fourier Transform and the wavelet Transform have been adapted to such context. Graph signal processing (GSP) is a new field with many potential applications on data exploration. In this dissertation we show how tools from graph signal processing can be used for visual analysis. Specifically, we proposed a data filtering method, based on spectral graph filtering, that led to high quality visualizations which were attested qualitatively and quantitatively. On the other hand, we relied on the graph wavelet transform to enable the visual analysis of massive time-varying data revealing interesting phenomena and events. The proposed applications of GSP to visually analyze data are a first step towards incorporating the use of this theory into information visualization methods. Many possibilities from GSP can be explored by improving the understanding of static and time-varying phenomena that are yet to be uncovered.

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