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23rd IEEE International Conference on image processing (ICIP)

作者： Li, Jizhou Luisier, Florian Blu, Thierry Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Roche Diagnost Hematol Boston MA USA

ISBN: (纸本)9781467399616

We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parametrize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds (LET). This parametrization is then optimized by minimizing a robust estimate of the mean squared error, the "Poisson unbiased risk estimate (PURE)". Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients. In contrast to existing approaches, the proposed algorithm merely amounts to solving a linear system of equations which has a fast and exact solution. Simulation experiments over various noise levels indicate that the proposed method outperforms current state-of-the-art techniques, in terms of both restoration quality and computational time.

关键词： image deconvolution Poisson noise unbiased risk estimate MSE estimation

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Hilbert-Huang Transform and its Application in Seismic signal processing

Hilbert-Huang Transform and its Application in Seismic Signa...

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International Conference on New Trends in signal processing (NTSP)

作者： Beresik, Roman Armed Forces Acad Gen MR Stefanik Dept Elect Liptovsky Mikulas Slovakia

ISBN: (纸本)9788080405298

The detection of targets in military and security applications involves the usage of sensor systems which consist of a variety of sensors such as seismic, acoustic, magnetic and image ones as well. In order to extract signal features, which characterize particular targets, using of appropriate signal processing algorithms is required. Seismic signals can be considered as nonstationary and nonlinear signals especially in near-field seismic zone. Most of the signal processing algorithms assumed that signals are linear and stationary. However, in many cases this assumption is not valid, especially in case of seismic signals generated by moving vehicles, walking persons or gunfire activity. There are several methods which can be used for seismic signal processing, like short-time Fourier transform (STFT), wavelet transform (WT) and Wigner-Ville distribution (WVD). The paper presents the concept of the seismic sensor system based on Micro-Electro-Mechanical-System (MEMS) sensor SF1500S.A dedicated to vehicle detection. The main part of the paper deals with application of the Hilbert-Huang transform (HHT) to seismic signal processing in time and time-frequency domain. In conclusion, the outcomes of experiments provide comparison of HHT and STFT efficiency in terms of seismic features description of moving vehicle.

关键词： seismic signal signal decomposition Hilbert-Huang transform time-frequency analysis vehicle detection

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BOLD效应fMRI图像的自适应阈值小波去噪方法

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计算机工程与应用 2017年第8期53卷 170-173,239页

作者：王泉德肖继来谢晟武汉大学电子信息学院武汉430070 北京中日友好医院北京100029

利用基于BOLD(Blood Oxygenation Level Dependent)效应的fMRI图像和两室模型可以定量计算脑氧摄取分数(Oxygen Extraction Fraction,OEF),在脑血管病的预测和诊断上有较大的临床应用价值。但由于BOLD效应fMRI图像的信噪比较低,研究并... 详细信息

利用基于BOLD(Blood Oxygenation Level Dependent)效应的fMRI图像和两室模型可以定量计算脑氧摄取分数(Oxygen Extraction Fraction,OEF),在脑血管病的预测和诊断上有较大的临床应用价值。但由于BOLD效应fMRI图像的信噪比较低,研究并设计有效的BOLD效应fMRI图像去噪算法,从而提高OEF计算结果的准确性是急需解决的问题。因此,设计了基于贝叶斯估计的自适应阈值小波去噪方法对BOLD效应fMRI图像进行分析和去噪,并将结果图像应用于OEF值的计算。实验结果表明该方法能有效提高OEF计算结果的准确性。

关键词：脑氧摄取分数图像去噪小波分析贝叶斯估计

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VEHICLE DETECTION IN AERIAL imageS USING MULTISCALE STRUCTURE ENHANCEMENT AND SYMMETRY 23

VEHICLE DETECTION IN AERIAL IMAGES USING MULTISCALE STRUCTUR...

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23rd IEEE International Conference on image processing (ICIP)

作者： Ram, Sundaresh Rodriguez, Jeffrey J. Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (纸本)9781467399616

Accurate detection and localization of vehicles in aerial images has a wide range of applications including urban planning, military reconnaissance, visual surveillance, and real-time traffic management. Automated detection of vehicles in aerial imagery is a challenging task, due to the density of vehicles on the road. the complexity of the surrounding environment in urban areas, and low spatial resolution of the image sensor array. We propose an automated method for detecting vehicles of varying sizes in low-resolution aerial imagery. First, we develop a new vehicle enhancement filter involving multiscale Hessian analysis. After thresholding, we refine the candidate vehicle detections based on analysis of bilateral symmetry. We show that our proposed method provides improved detection accuracy compared with existing vehicle detection algorithms for various low-resolution aerial images.

关键词： wavelet transform vehicle detection aerial imagery Hessian filter bilateral symmetry

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Guide to signals and Patterns in image processing: Foundations, Methods and applications

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2016年

作者： Apurba Das

ISBN: (纸本)9783319377995

This text reviews the field of digital image processing from the different perspectives offered by the separate domains of signal processing and pattern recognition. The book describes a rich array of applications, representing the latest trends in industry and academic research. To inspire further interest in the field, a selection of worked-out numerical problems is also included in the text. The content is presented in an accessible manner, examining each topic in depth without assuming any prior knowledge from the reader, and providing additional background material in the appendices. Features: covers image enhancement techniques in the spatial domain, the frequency domain, and the wavelet domain; reviews compression methods and formats for encoding images; discusses morphology-based image processing; investigates the modeling of object recognition in the human visual system; provides supplementary material, including MATLAB and C++ code, and interactive GUI-based modules, at an associated website.

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Spline and Spline wavelet Methods with applications to signal and image processing: Volume II Non-Periodic Splines

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2015年

作者： Amir Z Averbuch Pekka Neittaanmki Valery A Zheludev

ISBN: (纸本)9783319223025

This book presents various contributions of splines to signal and image processing from a unified perspective that is based on the Zak transform (ZT). It expands the methodology from periodic splines, which were presented in the first volume, to non-periodic splines. Together, these books provide a universal toolbox accompanied by MATLAB software for manipulating polynomial and discrete splines, spline-based wavelets, wavelet packets and wavelet frames for signal/ image processing applications. In this volume, we see that the ZT provides an integral representation of discrete and polynomial splines, which, to some extent, is similar to Fourier integral. The authors explore elements of spline theory and design, and consider different types of polynomial and discrete splines. They describe applications of spline-based wavelets to data compression. These splines are useful for real-time signal processing and, in particular, real-time wavelet and frame transforms. Further topics addressed in this volume include: "global" splines, such as interpolating, self-dual and smoothing, whose supports are infinite; the compactly supported quasi-interpolating and smoothing splines including quasi-interpolating splines on non-uniform grids; and cubic Hermite splines as a source for the design of multiwavelets and multiwavelet frames. Readers from various disciplines including engineering, computer science and mathematical information technology will find the descriptions of algorithms, applications and software in this book especially useful.

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p-exponent and p-leaders, Part II: Multifractal analysis. Relations to detrended fluctuation analysis

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PHYSICA A-STATISTICAL MECHANICS AND ITS applications 2016年 448卷 319-339页

作者： Leonarduzzi, R. Wendt, H. Abry, P. Jaffard, S. Melot, C. Roux, S. G. Torres, M. E. Univ Lyon ENS Lyon CNRS UMR 5672 Signal Syst & PhysPhys Dept Lyon France Univ Toulouse IRIT CNRS UMR 5505 Toulouse France Univ Paris Est Lab Anal & Math Appl CNRS UMR 8050 UPEC Creteil France Aix Marseille Univ CNRS Cent Marseille I2MUMR 7373 F-13453 Marseille France Univ Nacl Entre Rios Consejo Nacl Invest Cient & Tecn Rios Argentina

Multifractal analysis studies signals, functions, images or fields via the fluctuations of their local regularity along time or space, which capture crucial features of their temporal/spatial dynamics. It has become a standard signal and image processing tool and is commonly used in numerous applications of different natures. In its common formulation, it relies on the Holder exponent as a measure of local regularity, which is by nature restricted to positive values and can hence be used for locally bounded functions only. In this contribution, it is proposed to replace the Holder exponent with a collection of novel exponents for measuring local regularity, the p-exponents. One of the major virtues of p-exponents is that they can potentially take negative values. The corresponding wavelet-based multiscale quantities, the p-leaders, are constructed and shown to permit the definition of a new multifractal formalism, yielding an accurate practical estimation of the multifractal properties of real-world data. Moreover, theoretical and practical connections to and comparisons against another multifractal formalism, referred to as multifractal detrended fluctuation analysis, are achieved. The performance of the proposed p-leader multifractal formalism is studied and compared to previous formalisms using synthetic multifractal signals and images, illustrating its theoretical and practical benefits. The present contribution is complemented by a companion article studying in depth the theoretical properties of p-exponents and the rich classification of local singularities it permits. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multifractal analysis p-exponent wavelet p-leaders Negative regularity Multifractal detrended fluctuation analysis

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An Anatomization of Noise removal techniques on Medical images 1

An Anatomization of Noise removal techniques on Medical Imag...

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1st International Conference on Innovation and Challenges in Cyber Security (ICICCS)

作者： Pandey, Madhulika Bhatia, Madhulika Bansal, Abhay Amity Univ Dept Comp Sci & Engn Noida India Amity Univ Int Collaborat Noida India

ISBN: (纸本)9781509020843

In applications of signal processing such as medicine, communications and satellites, preprocessing is considered as a vital step which focuses on reduction or removal of the level of the noise contained in the image. The process of denoising helps in preserving the finer details and useful information. Medical images like MRI, CT and X-ray contain very fine details that need to be correct and free from noise so that the information and features of interests are not lost during the diagnosis. In this paper, various noise reduction techniques such as wavelet transform, Neural Network, PCA, ICA and mean and median filters over medical images has been discussed. In this paper we tried to highlight the strength and weakness of various noise removal techniques over processing of the medical images.

关键词： noise removal medical images wavelet transform PCA ICA median filter average median filter laplacian pyramid transform contourlet transform

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An Enhanced Hyper Spectral image (HSI) Compression Based On Residual Dependent Arithmetic Coder (RDAC)

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CURRENT MEDICAL IMAGING REVIEWS 2016年第3期12卷 178-188页

作者： Thiyagarajan, Senthivel Dhavamani, Gnanadurai Malathi, Somasundaram JP Coll Engn Dept Comp Sci & Engn Tenkasi India JP Coll Engn Dept Elect & Commun Engn Tenkasi India Sri Parasakthi Coll Women Dept Comp Sci Courtallam India

Background: The Hyper Spectral image (HSI) compression is a challenging and demanding task in many remote sensing applications, because it has the large hyperspectral data. Optical remote sensing is much increased due to newly imported sensor technologies and advancements. Lossy HSI compression is an essential part for long-terms spectral storage data. In this paper, we provide a new lossy HSI compression algorithm with the help of Residual Dependent Arithmetic Coder (RDAC). Methods: The main intention of this work is to reduce the complexity while compressing the large volume of data by compressing the spectral bands. Here, the Gray Level Co-occurrence Matrix (GLCM) technique is employed to extract the texture features of the given HSI band image. Then, the k-means clustering algorithm is employed to select the reference band in each cluster based on the cluster prominence value. Moreover, the RDAC is used to compress the reference band and the residual band information of each cluster. Finally, the HSI is decompressed with the help of compressed HSI band images. Results: In experiments, the performance of the proposed method is analyzed and evaluated in terms of Mean Squared Error (MSE), Peak signal-to-Noise Ratio (PSNR) and Compression Ratio (CR). Moreover, it is compared with some of the existing HSI compression techniques such as, Set Partitioning in Hierarchical Trees (SPIHT), Joint Photographic Expert Group (JPEG), Set Partitioning Embedded bloCK (3D-SPECK), Inverse wavelet Transform (IWT) and Reverse Karhunen-Loeve Transform (RKLT). Conclusion: This paper proposes a new RDAC technique for lossy HSI compression. For this purpose, different image processing techniques are used. In this analysis, it is proved that the proposed HSI compression technique provides the best results, when compared to the other techniques.

关键词： 3D-SPIHT and 3D-SPECK Bits Per Pixel (BPP) Compression Ratio (CR) Hyper Spectral image (HSI) JPEG 2000 K-Means clustering Mean Squared Error (MSE) Residual Dependent Arithmetic Coder (RDAC)

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image Sharpness Assessment Based on Local Phase Coherence and LAD Criterion 23

Image Sharpness Assessment Based on Local Phase Coherence an...

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23rd International Conference on Systems, signals and image processing (IWSSIP)

作者： Krzic, Ana Sovic Donlic, Matea Pejcinovic, Mihaela Sersic, Damir Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb 10000 Croatia Poslovna Inteligencija Zelinska 4 Zagreb 10000 Croatia

ISBN: (纸本)9781467395557

Edge preservation and precise restoration of details and sharp parts of images is an important issue in modern multimedia systems, 3D imaging, medical imaging and laser microscopy. Objective evaluation of image restoration algorithms in the sense of sharpness is crucial for choice of method to be used. Moreover, automated focus stacking, selective deblurring and other smart restoration methods rely on the proper sharpness measure. In this paper, a novel approach for image sharpness assessment is proposed. The approach is based on local phase coherence (LPC) in the complex wavelet domain. Using maximum pooling of eight LPC maps in eight spatial directions around each pixel, an image sharpness map is generated. Least absolute deviation criterion applied on sorted non-zero elements of the sharpness map results in the proposed global image sharpness measure. The proposed measure is better suited for applications, in comparison with competitive measures.

关键词： Sharpness Assessment Sharpness Measure Blurriness Local Phase Coherence

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