检索结果-内蒙古大学图书馆

7th European Workshop on Visual Information processing (EUVIP)

作者： Khan, Zohaib Amjad Kaaniche, Mounir Beghdadi, Azeddine Cheikh, Faouzi Alaya Univ Paris 13 L2TI Inst Galilee Paris France NTNU Norwegian Colour & Visual Comp Lab Gjovik Norway

ISBN: (纸本)9781538668979

The recent advances in 3D acquisition and display technologies have led to the use of stereoscopy for a wide range of applications. The quality assessment of such stereo data becomes of great interest especially when the reference image is not available. For this reason, we propose in this paper a no-reference 3D image quality assessment algorithm based on joint statistical modeling of the wavelet subband coefficients of the stereo pairs. More precisely, we resort to bivariate and multivariate statistical modeling of the texture images to build efficient statistical features. These features are then combined with the depth ones and used to predict the quality score based on machine learning tools. The proposed methods are evaluated on LIVE 3D database and the obtained results show the good performance of joint statistical modeling based approaches.

关键词： Stereo image image Quality No-reference Quality Assessment Multivariate modeling Statistical features

来源：评论

学校读者我要写书评

暂无评论

A Novel Diffusion-based Empirical Mode Decomposition Algorithm for signal and image Analysis

A Novel Diffusion-based Empirical Mode Decomposition Algorit...

引用

作者： Heming Wang University of Waterloo

学位级别：硕士

In the area of signal analysis and processing, the Fourier transform and wavelet trans- form are widely applied. Empirical Mode Decomposition(EMD) was proposed as an al- ternative frequency analysis tool. Although shown to be effective when analyzing non- stationary signals, the algorithmic nature of EMD makes the theoretical analysis and for- mulation difficult. Futhermore, it has some limitations that affect its performance. In this thesis, we introduce some methods to extend or modify EMD, in an effort to provide a rigorous mathematical basis for it, and to overcome its shortcomings. We propose a novel diffusion-based EMD algorithm that replaces the interpolation process by a diffusion equation, and directly construct the mean curve (surface) of a signal (image). We show that the new method simplifies the mathematical analysis, and provides a solid theory that interprets the EMD mechanism. In addition, we apply the new method to the 1D and 2D signal analysis showing its possible applications in audio and image signal processing. Finally, numerical experiments for synthetic and real signals (both 1D and 2D) are presented. Simulation results demonstrate that our new algorithm can overcome some of the shortcomings of EMD, and require much less computation time.

关键词：

来源：评论

学校读者我要写书评

暂无评论

image super resolution using fractional discrete wavelet transformand fractional fast fourier transform 2

Image super resolution using fractional discrete wavelet tra...

引用

2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2018

作者： Ray Choudhury, Anita Dahake, Vijay R. Department of Electronics and Telecommunication RamaraoAdik Institute of Tehnology Nerul Navi Mumbai Maharashtra India

ISBN: (纸本)9781538614426

Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images. In various image processing fields super resolution is playing a very crucial part. The single-frame as well as multi-frame both the methods in SR are widely used in multiple day to day applications and systems from last few years. In this work, we are proposing an image resolution enhancement technique which has its basis on interpolation of the high-frequency sub-band images obtained by fractional discrete wavelet transform (FDWT). HereFDWT is used to decompose an input low-resolution image into different subbands. Then, interpolation between the high-frequency subband images and the input image takes place, followed by combining all these images to generate a new high-resolution image by using inverse FDWT. Directional selectivity which is provided by FDWT actually produces resolution enhancement, where the high-frequency subbands in different directions contribute to the sharpness of the high-frequency details such as edges. The quantitative peak signal-to-noise ratio (PSNR) and visual results show the superiority of the proposed technique over the conventional bicubic interpolation image resolution enhancement techniques. © 2018 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Compressive hyperspectral imaging using total variation minimization 22

Compressive hyperspectral imaging using total variation mini...

引用

Conference on Imaging Spectrometry XXII - applications, Sensors, and processing

作者： Lee, Dennis J. Shields, Eric A. Sandia Natl Labs 1515 Eubank Blvd Albuquerque NM 87123 USA

ISBN: (纸本)9781510621084

Compressive sensing shows promise for sensors that collect fewer samples than required by traditional Shannon-Nyquist sampling theory. Recent sensor designs for hyperspectral imaging encode light using spectral modulators such as spatial light modulators, liquid crystal phase retarders, and Fabry-Perot resonators. The hyperspectral imager consists of a filter array followed by a detector array. It encodes spectra with less measurements than the number of bands in the signal, making reconstruction an underdetermined problem. We propose a reconstruction algorithm for hyperspectral images encoded through spectral modulators. Our approach constrains pixels to be similar to their neighbors in space and wavelength, as natural images tend to vary smoothly, and it increases robustness to noise. It combines L-1 minimization in the wavelet domain to enforce sparsity and total variation in the image domain for smoothness. The alternating direction method of multipliers (ADMM) simplifies the optimization procedure. Our algorithm constrains encoded, compressed hyperspectral images to be smooth in their reconstruction, and we present simulation results to illustrate our technique. This work improves the reconstruction of hyperspectral images from encoded, multiplexed, and sparse measurements.

关键词： Hyperspectral imaging Compressive sensing Total variation Alternating direction method of multipliers ADMM Fabry-Perot resonators wavelet transform Remote sensing Biomedical imaging Spectroscopic imaging

来源：评论

学校读者我要写书评

暂无评论

Deep Convolutional Neural Network for Ultrasound image Enhancement

Deep Convolutional Neural Network for Ultrasound Image Enhan...

引用

IEEE International Ultrasonics Symposium (IUS)

作者： Perdios, Dimitris Vonlanthen, Manuel Besson, Adrien Martinez, Florian Arditi, Marcel Thiran, Jean-Philippe Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 Lausanne Switzerland Univ Hosp Ctr CHUV Dept Radiol Lausanne Switzerland Univ Lausanne UNIL Lausanne Switzerland

ISBN: (纸本)9781538634257

The problem of improving image quality in ultrafast ultrasound (US) imaging by means of regularized iterative algorithms has raised a vast interest in the US community. These approaches usually rely on standard image processing priors, such as wavelet sparsity, which are of limited efficacy in the context of US imaging. Moreover, the high computational complexity of iterative approaches make them difficult to deploy in real-time applications. We propose an approach which relies on a convolutional neural network trained exclusively on a simulated dataset for the purpose of improving images reconstructed from a single plane wave (PW) insonification. We provide extensive results on numerical and in vivo data from the plane wave imaging challenge (PICMUS). We show that the proposed approach can be applied in real-time settings, with an increase in contrast-to-noise ratio of more than 8.4 dB and an improvement of the lateral resolution by at least 25 %.

关键词： image enhancement ultrasound imaging image processing deep learning

来源：评论

学校读者我要写书评

暂无评论

wavelet TRANSFORM IN FINANCIAL TIME SERIES ANALYSIS: DENOISING AND FORECAST

WAVELET TRANSFORM IN FINANCIAL TIME SERIES ANALYSIS: DENOISI...

引用

作者： Andriy Savka Kent State University

学位级别：硕士

wavelet transform, based on the theory of Fourier transform, is a powerful tool of frequency analysis, which allows to switch from time domain of time series to its frequency- representation for further study. wavelet transformation techniques are widely used in signal processing, utilized to compress and efficiently store signal and image information with min- imum loss of important details. Most economic and financial time series contain layered information about trend of the related economic phenomena, seasonal variation, and noise. The latter is usually associated with unexplained uncertainty shocks. As these three com- ponents of economic or financial time series have different frequencies, it is natural to apply frequency analysis tools to extract useful information and reduce noise (unimportant compo- nent of time series). The purpose of this thesis is to review recent study on wavelet transform techniques and their applications for denoising in economic and financial time series. The thesis begins from overview of wavelets, their connection to Fourier transform, and place in frequency analysis study. Then, Dyadic multiresolution analysis as a basic framework of discrete wavelet analysis is discussed. Next, wavelet denoising is discussed. Further, statistical methods of time series analysis are introduced. The research concludes with empirical application of denoising technique using discrete wavelet transform to analysis of the Standard&Poor's 500 stock prices index and West Texas Intermediate crude oil prices on the U. S. market.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Visual analytics via graph signal processing

Visual analytics via graph signal processing

引用

作者： Dal Col Júnior, Alcebíades Universidade de Lisboa

The classical wavelet transform has been widely used in image and signal processing, where a signal is decomposed into a combination of basis signals. By analyzing the individual contribution of the basis signals, one can infer properties of the original signal. This dissertation presents an overview of the extension of the classical signal processing theory to graph domains. Specifically, we review the graph Fourier transform and graph wavelet transforms both of which based on the spectral graph theory, and explore their properties through illustrative examples. The main features of the spectral graph wavelet transforms are presented using synthetic and real-world data. Furthermore, we introduce in this dissertation a novel method for visual analysis of dynamic networks, which relies on the graph wavelet theory. Dynamic networks naturally appear in a multitude of applications from different domains. Analyzing and exploring dynamic networks in order to understand and detect patterns and phenomena is challenging, fostering the development of new methodologies, particularly in the field of visual analytics. Our method enables the automatic analysis of a signal defined on the nodes of a network, making viable the detection of network properties. Specifically, we use a fast approximation of the graph wavelet transform to derive a set of wavelet coefficients, which are then used to identify activity patterns on large networks, including their temporal recurrence. The wavelet coefficients naturally encode spatial and temporal variations of the signal, leading to an efficient and meaningful representation. This method allows for the exploration of the structural evolution of the network and their patterns over time. The effectiveness of our approach is demonstrated using different scenarios and comparisons involving real dynamic networks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Complex Deep Learning Models for Denoising of Human Heart ECG signals

arXiv

引用

arXiv 2019年

作者： Arsene, Corneliu T. School of Electrical and Electronics Engineering University of Manchester Manchester United Kingdom

: Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper presents several DL models namely Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Restricted Boltzmann Machine (RBM) together with the more conventional filtering methods (low pass filtering, high pass filtering, Notch filtering) and the standard wavelet-based technique for denoising EEG signals. These methods are trained, tested and evaluated on different synthetic and real ECG datasets taken from the MIT PhysioNet database and for different simulation conditions (i.e. various lengths of the ECG signals, single or multiple records). The results show the CNN model is a performant model that can be used for off-line denoising ECG applications where it is satisfactory to train on a clean part of an ECG signal from an ECG record, and then to test on the same ECG signal, which would have some high level of noise added to it. However, for real-time applications or near-real time applications, this task becomes more cumbersome, as the clean part of an ECG signal is very probable to be very limited in size. Therefore the solution put forth in this work is to train a CNN model on 1 second ECG noisy artificial multiple heartbeat data (i.e. ECG at effort), which was generated in a first instance based on few sequences of real signal heartbeat ECG data (i.e. ECG at rest). Afterwards it would be possible to use the trained CNN model in real life situations to denoise the ECG signal. This corresponds also to reality, where usually the human is put at rest and the ECG is recorded and then the same human is asked to do some physical exercises and the ECG is recorded at effort. The quality of results is assessed visually but also by using the Root M

关键词： signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Smoke Detection in Compressed Video 41

Smoke Detection in Compressed Video

引用

Conference on applications of Digital image processing XLI

作者： Toreyin, Behcet Ugur Istanbul Tech Univ Inst Informat Signal Proc Computat Intelligence Grp SP4CING TR-34469 Istanbul Turkey

ISBN: (数字)9781510620766

ISBN: (纸本)9781510620766

Early detection of fires is an important aspect of public safety. In the past decades, devices and systems have been developed for volumetric sensing of fires using non-conventional techniques, such as, computer vision based methods and pyro-electric infrared sensors. These systems pose an alternative for more commonly used point detectors, which suffer from transport delay in large and open areas. The ubiquity of computing and recent developments on novel hardware alternatives, like memristor crossbar arrays, promise an increase in the number of deployments of such systems. Existing video-based methods have been developed for the analysis of uncompressed spatio-temporal sequences. In order to respond the growing demand of such systems, techniques specifically aimed at analyzing compressed domain video streams should be developed for early fire detection purposes. In this paper, a Markov model and wavelet transform based technique is proposed to further improve the current state-of-the-art methods for video smoke detection by detecting signs of smoke existence in the MJPEG2000 compressed video.

关键词： Smoke detection fire detection compressed domain cognition compressed domain video analysis MJPEG2000 computer vision Markov model crossbar memristor array

来源：评论

学校读者我要写书评

暂无评论

HS remote sensing image restoration using fusion with MS images by EM algorithm

引用

IET signal processing 2017年第1期11卷 95-103页

作者： Ansari, Amir Danyali, Habibollah Helfroush, Mohammad Sadegh Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Remote sensing images are widely used for different areas from mineral exploration to agricultural applications and poor quality of hyperspectral (HS) images will directly have adverse effect on these applications. In this study, a method is proposed to restore degraded HS images. To achieve this aim, another multispectral (MS) observation of the same scene is supposed to be available and restoration is fulfilled by fusion of HS images and MS images. The proposed method gains maximum a posteriori estimation and is based on expectation maximisation algorithm. Deblurring and denoising are performed separately. Deblurring is done in spatial domain via non-overlapping blocks, whereas denoising is implemented in wavelet domain. To represent the coefficients in wavelet domain, instead of multinormal model, Gaussian scale mixture is exploited. The proposed method is validated on airborne visible/infrared imaging spectrometer (AVIRIS) and HS digital imagery collection experiment (HYDICE) databases and experimental results signify that the proposed method outperforms state-of-the-art techniques cited in the literature and signal-to-noise ratio is improved as much as 15.71dB for Moffett database and 16.26dB for HYDICE database.

关键词： remote sensing image restoration image fusion hyperspectral imaging expectation-maximisation algorithm image denoising wavelet transforms Gaussian processes mixture models geophysical image processing HS remote sensing image restoration MS images EM algorithm mineral exploration agricultural application hyperspectral image quality degraded HS image restoration multispectral observation HS image-MS image fusion maximum a posteriori estimation expectation maximisation algorithm deblurring denoising spatial domain nonoverlapping blocks wavelet domain multinormal model Gaussian scale mixture airborne visible-infrared imaging spectrometer HS digital imagery collection experiment HYDICE database signal-to-noise ratio Moffett database

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：