Wavelet Analysis of Space Solar Telescope Images

The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.

Medical Image Segmentation Based on Wavelet Analysis and Gradient Vector Flow

Medical image segmentation is one of the key technologies in computer aided diagnosis. Due to the complexity and diversity of medical images, the wavelet multi-scale analysis is introduced into GVF (gradient vector flow) snake model. The modulus values of each scale and phase angle values are calculated using wavelet transform, and the local maximum points of modulus values, which are the contours of the object edges, are obtained along phase angle direction at each scale. Then, location of the edges of the object and segmentation is implemented by GVF snake model. The experiments on some medical images show that the improved algorithm has small amount of computation, fast convergence and good robustness to noise.

An Automated Brain Image Analysis System for Brain Cancer using Shearlets

In this paper,an Automated Brain Image Analysis(ABIA)system that classifies the Magnetic Resonance Imaging(MRI)of human brain is presented.The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis.The Non-Subsampled Shearlet Transform(NSST)that captures more visual information than conventional wavelet transforms is employed for feature extraction.As the feature space of NSST is very high,a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies.A combination of features that includes Gray Level Co-occurrence Matrix(GLCM)based features,Histograms of Positive Shearlet Coefficients(HPSC),and Histograms of Negative Shearlet Coefficients(HNSC)are estimated.The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers;k-Nearest Neighbor(kNN),Naive Bayes(NB)and Support Vector Machine(SVM)classifiers.The output of individual trained classifiers for a testing input is hybridized to take a final decision.The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data(REMBRANDT)database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.

Coherency Evaluation of Spatiotemporal Neural Activities in the Molluscan Olfactory Center Applying Extracellular Recording with Wavelet Analysis

The procerebrum (PC) of the land slug Limax is the olfactory center involved in olfactory discrimination and learning. In the PC, an oscillation of local field potential (LFP) with 0.5 - 1 Hz is observed by electrophysiological extracellular recording. Additionally, spatiotemporal neural activities in the PC have been examined using optical recordings. However, extracellular recording is preferable to measure neural activities for a long time with a high speed, while it is not abundant in spatial information. In this study, we therefore attempted to elicit spatial information from extracellular recording. For this purpose, we evaluated spatial information included in the LFP compared with the spatiotemporal neural activities measured by the fluorescent voltage imaging. As a result, aversive odors induced the coherent spatiotemporal neural activities in the PC, and the increase in coherency was observed as a change in the LFP waveform. It was also evaluated as a decrease in entropy by analyzing the LFP oscillation patterns and wavelet analysis. Thus, although the LFP provides only one series of signals, the coherency of the spatiotemporal neural activities in the PC can be evaluated by applying extracellular recording with wavelet analysis.

Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classification

Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine （SVM） as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer II hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four principal components from principal component analysis and wavelet texture provides the highest accuracy （97.06%）. Multi-kernel SVM is therefore an effective approach to improve the accuracy of hyperspectral image classification and to expand possibilities for remote sensing image interpretation and application.

SAR image denoising based on wavelet-fractal analysis

Wavelet-fractal based SAR （synthetic aperture radar） image processing is one of the advanced technologies in image processing. The main concept of analysis is that after wavelet transformation, multifractal spectrum of the signal is different from that of noise. This difference is used to alleviate the noise produced by SAR image.The method to denoise SAR image using the process based on wavelet-fractai analysis is discussed in detail. Essentially, the present method focuses on adjusting the Hoelder exponent α of multifractal spectrum. After simulation, α should be adjusted to 1.72-1.73. The more the value of α exceeds 1.73, the less distinctive the edges of SAR image become. According to the authors denoising is optimal at α=1.72-1.73. In other words, when α =1.72-1.73, a smooth and denoised SAR image is produced.

Application and Analysis of Wavelet Transform in Image Edge Detection

For the image processing technology, technicians have been looking for a convenient and simple detection method for a long time, especially for the innovation research on image edge detection technology. Because there are a lot of original information at the edge during image processing, thus, we can get the real image data in terms of the data acquisition. The usage of edge is often in the case of some irregular geometric objects, and we determine the contour of the image by combining with signal transmitted data. At the present stage, there are different algorithms in image edge detection, however, different types of algorithms have divergent disadvantages so It is diffi cult to detect the image changes in a reasonable range. We try to use wavelet transformation in image edge detection, making full use of the wave with the high resolution characteristics, and combining multiple images, in order to improve the accuracy of image edge detection.

Laser speckle imaging and wavelet analysis of cerebral blood flow associated with the opening of the blood–brain barrier by sound

The cerebral blood flow（CBF） alterations related to sound-induced opening of the blood–brain barrier（BBB） in adult mice are studied using laser speckle contrast imaging（LSCI） and wavelet analysis of vascular physiology.The results clearly show that the opening of the BBB is accompanied by the changes of venous but not microvessel circulation in the brain. The elevation of the BBB permeability is associated with the decrease of venous CBF and the increase of its complexity. These data suggest that the cerebral veins rather than microvessels are sensitive components of the CBF related to the opening BBB.

Analysis of droplet transfer of pulsed MIG welding based on electrical signal and high-speed photography

In order to study how welding parameters affect welding quality and droplet transfer, a synchronous acquisition and analysis system is established to acquire and analyze electrical signal and instantaneous images of droplet transfer simultaneously, which is based on a self-developed soft-switching inverter. On the one hand, welding current and voltage signals are acquired and analyzed by a self-developed dynamic wavelet analyzer. On the other hand, images are filtered and optimized after they are captured by high-speed camera. The results show that instantaneous waveforms and statistical data of electrical signal contribute to make an overall assessment of welding quality, and that optimized high-speed images allow a visual and clear observation of droplet transfer process. The analysis of both waveforms and images leads to a further research on droplet transfer mechanism and provides a basis for precise control of droplet transfer.

Fault Diagnosis for IC Engines Using Wavelet Packet and Image Processing

There are few applications of image processing technology for diagnosing andstate monitoring for internal combustion (IC) engines, which is discussed in detail in this paper.The time-frequency distribution images of cylinder head vibration signals are obtained bydecomposing them with a wavelet packet algorithm. It is the first time that we look attime-frequency distribution images from the point of images. Based on this, a new method forapplying image processing technology for diagnosing and state monitoring for internal combustionengines is presented in this paper. A valve fault diagnosis model is set up by image matching, whichis realized on a four-stroke, six-cylinder diesel engine. At the same time, some notes arepresented in this paper. It has been proved that it is of no good effect to diagnose with histogramsof time-frequency images generated by cylinder head vibration signals that have been processed witha wavelet packet algorithm. The reason is given in this paper. Comparisons of diagnosing effect arecarried out between noise-added signals and original signals. It has little effect on diagnosingresults after signals have been added with noise. The results show that this method has a clearphysical meaning and is of good engineering practicability, feasibility, good precision and highspeed.

Multi-Focus Image Fusion Based on Wavelet Transformation

In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.

Research on Wavelet-Based Algorithm for Image Contrast Enhancement

A novel wavelet-based algorithm for image enhancement is proposed in the paper. On the basis of multiscale analysis, the proposed algorithm solves efficiently the problem of noise over-enhancement, which commonly occurs in the traditional methods for contrast enhancement. The decomposed coefficients at same scales are processed by a nonlinear method, and the coefficients at different scales are enhanced in different degree. During the procedure, the method takes full advantage of the properties of Human visual system so as to achieve better performance. The simulations demonstrate that these characters of the proposed approach enable it to fully enhance the content in images, to efficiently alleviate the enhancement of noise and to achieve much better enhancement effect than the traditional approaches. Key words wavelet transform - image contrast enhancement - multiscale analysis CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (69931010)Biography: Wu Ying-qian (1974-), male, Ph. D, research direction: image processing, image compression and wavelet.

Analysis of Turbulence Structure in the Stirred Tank with a Deep Hollow Blade Disc Turbine by Time-resolved PIV

The turbulence structure in the stirred tank with a deep hollow blade(semi-ellispe) disc turbine(HEDT) was investigated by using time-resolved particle image velocimetry(TRPIV) and traditional PIV.In the stirred tank,the turbulence generated by blade passage includes the periodic components and the random turbulent ones.Traditional PIV with angle-resolved measurement and TRPIV with wavelet analysis were both used to obtain the random turbulent kinetic energy as a comparison.The wavelet analysis method was successfully used in this work to separate the random turbulent kinetic energy.The distributions of the periodic kinetic energy and the random turbulent kinetic energy were obtained.In the impeller region,the averaged random turbulent kinetic energy was about 2.6 times of the averaged periodic one.The kinetic energies at different wavelet scales from a6 to d1 were also calculated and compared.TRPIV was used to record the sequence of instantaneous velocity in the impeller stream.The evolution of the impeller stream was observed clearly and the sequence of the vorticity field was also obtained for the identification of vortices.The slope of the energy spectrum was approximately-5/3 in high frequency representing the existence of inertial subrange and some isotropic properties in stirred tank.From the power spectral density(PSD) ,one peak existed evidently,which was located at f0(blade passage frequency) generated by the blade passage.

THE ANALYSIS OF ADHESIVE DISTRIBUTION ON PARTICLE SURFACE

With adopting the Wavelet analysis theory, the adhesive distribution state on particle surfce was studied during the particleboard manufacturing. Using the image processing method, the ratio of adhesive covering on the particle surface was measured. The image of Wavelet transform, marginal texture check and marginal texture characteristic parameters were obtained.This method was very useful to study the mechanism of particles gluing and to improve the quality of particleboard.

A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Posteriori(MAP)approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients.For the approx imation coefficients,a new fusion rule based on the Principal Component Analysis(PCA)is applied.We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method.The obt ained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics.Robustness of the proposed method is further tested against different types of noise.The plots of fusion met rics establish the accuracy of the proposed fusion method.

Wavelet Transform for Image Compression Using Multi-Resolution Analytics: Application to Wireless Sensors Data

The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins include data from the various social media, footages from video cameras, wireless and wired sensor network measurements, data from the stock markets and other financial transaction data, supermarket transaction data and so on. The aforementioned data may be high dimensional and big in Volume, Value, Velocity, Variety, and Veracity. Hence one of the crucial challenges is the storage, processing and extraction of relevant information from the data. In the special case of image data, the technique of image compressions may be employed in reducing the dimension and volume of the data to ensure it is convenient for processing and analysis. In this work, we examine a proof-of-concept multiresolution analytics that uses wavelet transforms, that is one popular mathematical and analytical framework employed in signal processing and representations, and we study its applications to the area of compressing image data in wireless sensor networks. The proposed approach consists of the applications of wavelet transforms, threshold detections, quantization data encoding and ultimately apply the inverse transforms. The work specifically focuses on multi-resolution analysis with wavelet transforms by comparing 3 wavelets at the 5 decomposition levels. Simulation results are provided to demonstrate the effectiveness of the methodology.

开放式超低场移动磁共振系统的电磁干扰抑制方法

为了发挥超低场磁共振成像可移动化的优势,传统的被动电磁屏蔽方法应该被主动电磁干扰抑制方法所替代。该文首先分析了电磁干扰传输路径并发现人体耦合为主要传输路径。针对性地设计了用以采集人体耦合电磁干扰的“戒指”型电磁干扰检测线圈,并配合平面及螺线管型线圈共同构成了电磁干扰参考通道。该文提出相关窗口概念:一定采集间隔内的参考电磁干扰信号仍有助于更加精确地估计某一时刻核磁通道电磁干扰信号。选定各通道K空间外围数据为校准数据,由核磁通道校准数据构建校准目标矩阵;由相关窗口内参考通道校准数据构建校准源矩阵。对上述两个矩阵进行小波分解后并通过最小二乘法求得各层转移系数。由参考电磁干扰信号在相关窗口内构建重构源矩阵,并由各层转移系数求得核磁通道电磁干扰拟合值。最后从受污染的核磁信号减去求得的拟合值,实现主动降噪。一维信号以及人体成像实验表明,该主动电磁干扰系统可大幅降低外界电磁干扰对图像质量的影响,使得磁共振扫描仪可在无屏蔽的开放环境中工作。

东非大裂谷的地壳和上地幔密度结构

利用卫星重力场数据和小波多尺度分析方法对东非大裂谷进行三维密度结构成像,取得了东非大裂谷地壳和上地幔多个深度等效层上的密度扰动图像,为东非大裂谷岩石圈结构和动力学的研究提供了重要佐证。结果表明,东非大裂谷中段的西支裂谷与东支裂谷的幔源熔体同源,但是西支裂谷发育较不充分、形成较晚。东非大裂谷的动力学模式为熔流体上涌的树形分叉模式,其要点包括:(1)低密度流体在软流圈大面积上涌;(2)流体在岩石圈继续上涌,部分转化为基性岩浆岩,平面面积缩小;(3)进入地壳后上涌熔流体分叉成多支,平面总面积进一步缩小;(4)熔流体上涌到上地壳后仅在裂谷带活动,反映为火山链和玄武岩带。

基于两维WAVELET分解的纹理图像分割方法

提出了一种纹理图像的分割方法，主要利用ＷＡＶＥＬＥＴ变换的多分辨率分析的特性，通过两维分解抽取图像的纹理特征，并对图像小窗口区域的特征进行聚类，该聚类结果可作为多层ＢＰ（Ｂａｃｋｐｒｏｐａｇａｔｉｏｎ）网权值学习的训练样本，进而利用ＢＰ网对各小窗口的特征进行分类以实现纹理图像的分割，实验证明，该方法对于纹理图像具有较好的分割效果。

FAST TEXT LOCATION BASED ON DISCRETE WAVELET TRANSFORM

The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, the text is fast detected from complex background images stored in the compressed format such as JPEG2000 without full decompress. Compared with some traditional character location methods, the proposed scheme has the advantages of low computational cost, robust to size and font of characters and high accuracy. Preliminary experimental results show that the proposed scheme is efficient and effective.