Application of radial basis functions to evolution equations arising in image segmentation

In this paper, radial basis functions are used to obtain the solution of evolution equations which appear in variational level set method based image segmentation. In this method, radial basis functions are used to interpolate the implicit level set function of the evolution equation with a high level of accuracy and smoothness. Then, the original initial value problem is discretized into an interpolation problem. Accordingly, the evolution equation is converted into a set of coupled ordinary differential equations, and a smooth evolution can be retained. Compared with finite difference scheme based level set approaches, the complex and costly re-initialization procedure is unnecessary. Numerical examples are also given to show the efficiency of the method.     

Range estimation by Doppler of multi-linein radiated noise spectrum

A method by applying Doppler frequency shift of multi-line in radiated noise spectrum to estimate the vertical range from a receiver to a moving vessel, which is supposed to move along a straight line at a constant velocity, is developed. This method is based on passive ranging by a single sensor and the depth of the sea and other environment parameters are not necessarily known. First the Wigner-Ville distribution is used as the instantaneous frequency estimator to find out the instantaneous frequencies of the muti-lines as a signal. Then define Doppler frequency shift basis functions, based on an algorithm called matching pursuit, by a searching strategy of variable span, and explore the minimum spatial distance between the signal and the Doppler frequency shift basis functions in a five-dimension space. The basis function of the obtained minimum spatial distance corresponds to the estimation of range and speed of the moving vessel. Computer simulations yield statistics errors in the range and speed estimates with differing intensities of noise. If the white noise deviation is less than 10% of the maximum Doppler frequency shift and time-window width is 1.47 times of reference-duration, relative error of range estimate is less than 5.4% and relative error of speed estimate is less than 1.4%. This estimation method has been tested and the result conforms to data collected during an experiment on the sea, the estimated speed is 52 knots and the estimated range is 42 m. The single point passive ranging method can be used for ranging purposes in sonar-buoys, mines, movement analysis of an underwater object in underwater acoustics experiment, and sound source level measurements.     

Application of Tikhonov regularization method to wind retrieval from scatterometer data Ⅱ: cyclone wind retrieval with consideration of rain

According to the conclusion of the simulation experiments in paper I, the Tikhonov regularization method is applied to cyclone wind retrieval with a rain-effect-considering geophysical model function (called GMF+Rain). The GMF+Rain model which is based on the NASA scatterometer-2 (NSCAT2) GMF is presented to compensate for the effects of rain on cyclone wind retrieval. With the multiple solution scheme (MSS), the noise of wind retrieval is effectively suppressed, but the influence of the background increases. It will cause a large wind direction error in ambiguity removal when the background error is large. However, this can be mitigated by the new ambiguity removal method of Tikhonov regularization as proved in the simulation experiments. A case study on an extratropical cyclone of hurricane observed with SeaWinds at 25-km resolution shows that the retrieved wind speed for areas with rain is in better agreement with that derived from the best track analysis for the GMF+Rain model, but the wind direction obtained with the two-dimensional variational (2DVAR) ambiguity removal is incorrect. The new method of Tikhonov regularization effectively improves the performance of wind direction ambiguity removal through choosing appropriate regularization parameters and the retrieved wind speed is almost the same as that obtained from the 2DVAR.     

Model/data comparison of typhoon-generated noise

Ocean noise recorded during a typhoon can be used to monitor the typhoon and investigate the mechanism of the windgenerated noise. An analytical expression for the typhoon-generated noise intensity is derived as a function of wind speed.A "bi-peak" structure was observed in an experiment during which typhoon-generated noise was recorded. Wind speed dependence and frequency dependence were also observed in the frequency range of 100 Hz–1000 Hz. The model/data comparison shows that results of the present model of 500 Hz and 1000 Hz are in reasonable agreement with the experimental data, and the typhoon-generated noise intensity has a dependence on frequency and a power-law dependence on wind speed.     

Application of the Tikhonov regularization method to wind retrieval from scatterometer data I. Sensitivity analysis and simulation experiments

Scatterometer is an instrument which provides all-day and large-scale wind field information, and its application especially to wind retrieval always attracts meteorologists. Certain reasons cause large direction error, so it is important to find where the error mainly comes. Does it mainly result from the background field, the normalized radar cross-section (NRCS) or the method of wind retrieval? It is valuable to research. First, depending on SDP2.0, the simulated `true' NRCS is calculated from the simulated 'true' wind through the geophysical model function NSCAT2. The simulated background field is configured by adding a noise to the simulated 'true' wind with the non-divergence constraint. Also, the simulated 'measured' NRCS is formed by adding a noise to the simulated 'true' NRCS. Then, the sensitivity experiments are taken, and the new method of regularization is used to improve the ambiguity removal with simulation experiments. The results show that the accuracy of wind retrieval is more sensitive to the noise in the background than in the measured NRCS; compared with the two-dimensional variational (2DVAR) ambiguity removal method, the accuracy of wind retrieval can be improved with the new method of Tikhonov regularization through choosing an appropriate regularization parameter, especially for the case of large error in the background. The work will provide important information and a new method for the wind retrieval with real data.     

Noise Shielding Using Acoustic Metamaterials

We exploit theoretically a class of rectangular cylindrical devices for noise shielding by using acoustic metamateriais. The function of noise shielding is justified by both the far-field and near-field full-wave simulations based on the finite element method. The enlargement of equivalent acoustic scattering cross sections is revealed to be the physical mechanism for this function. This work makes it possible to design a window with both noise shielding and air flow.     

A normalized data-reusing least-mean-square algorithm of noise cancellation for magnetocardiography

An adaptive filter based on Data-Reusing Least-Mean-Square algorithm has been proposed and applied in magnetocardiography (MCG) to suppress ambient noise, Numerical simulation studies indicate that the adaptive filter is a powerful noise suppresser for correlated interferences, especially for those with amplitude changing and time delay.Also the filter has a wide frequency bandwidth. With this filter, the signal-to-noise ratio of an MCG is improved to the intrinsic noise level, The periodic average method for further improvement of the noise level in MCG is also discussed.     

Global vector-field reconstruction of nonlinear dynamical systems from a time series with SVD method and validation with Lyapunov exponents

A method for the global vector-field reconstruction of nonlinear dynamical systems from a time series is studied in this paper. It employs a complete set of polynomials and singular value decomposition (SVD) to estimate a standard function which is certtral to the algorithm. Lyapunov exponents and dimension, calculated from the differential equations of a standard system, are used for the validation of the reconstruction. The algorithm is proven to be practical by applying it to a Roessler system.     

Margolus–Levitin speed limit across quantum to classical regimes based on trace distance

The classical version of Mandelstam–Tamm speed limit based on the Wigner function in phase space was reported by Shanahan et al. [Phys. Rev. Lett. 120 070401(2018)]. We present the Margolus–Levitin speed limit across the quantumto-classical transition in phase space based on the trace distance. The Margolus–Levitin speed limit is set by the Schatten L_1 norm of the generator of time-dependent evolution for both the quantum and classical domains. As an example, the time-dependent harmonic oscillator is considered to illustrate the result.     

Point spread function modeling and image restoration for cone-beam CT

X-ray cone-beam computed tomography(CT) has such notable features as high efficiency and precision,and is widely used in the fields of medical imaging and industrial non-destructive testing, but the inherent imaging degradation reduces the quality of CT images. Aimed at the problems of projection image degradation and restoration in cone-beam CT, a point spread function(PSF) modeling method is proposed first. The general PSF model of conebeam CT is established, and based on it, the PSF under arbitrary scanning conditions can be calculated directly for projection image restoration without the additional measurement, which greatly improved the application convenience of cone-beam CT. Secondly, a projection image restoration algorithm based on pre-filtering and pre-segmentation is proposed, which can make the edge contours in projection images and slice images clearer after restoration, and control the noise in the equivalent level to the original images. Finally, the experiments verified the feasibility and effectiveness of the proposed methods.     

基于迭代拟合的集装箱角件边缘检测算法

噪声通常是影响集装箱角件图像中低层次语义信息提取精度的重要因素,传统的边缘检测算法通常通过改进滤波器和阈值来消除图像中的物理噪声和环境噪声,但是却无法去除边缘检测后的噪声,为解决这一问题,提出了一种基于迭代拟合的边缘检测算法。首先,对角件图像进行一系列预处理操作获取边缘点集,其次,使用拟合算法处理点集并且得到函数表达式,然后定义偏差值度量并计算,用于衡量目标点集到拟合或者检测结果的偏差,最后,去除定义下距离拟合结果最远的指定数量的点,如此迭代拟合直至评价函数收敛。实验结果与分析表明,该算法可以有效地去除边缘点集中的非真实边缘点,相比于传统的边缘检测算法更能去除特殊噪声,算法具有收敛速度快、准确率较高、灵活性好等特点。     

Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.     

一种基于双分支改良编解码器的图像去噪算法

针对传统图像去噪算法多噪声去除难，深层卷积神经网络去噪模型网络复杂、训练时间长等问题，提出一种基于自编码器结构的双分支改良编解码网络，实现高效图像去噪。双分支结构之一采用降-升采样实现点噪声消除，另一分支专注于宏观的图像修复和伪像去除，后端利用残差结构进行整合，实现数字图像混合噪声去噪。实验结果显示:对于含有标准差为15，均值为0的高斯噪声、噪声密度为5%的椒盐噪声和散粒噪声的混合噪声图像测试集，实验去噪效果相较于输入混合噪声图像峰值信噪比，平均提升了5.3%。与12层全卷积神经网络相比，去噪效果相当，训练速度提升了25.4%，体现了其“轻量级”的优点。实验表明:该方法相较于深层卷积神经网络，训练速度快，网络简单；相较于传统图像去噪算法，噪声去除效果也较为明显。该算法可应用于轻量级视觉平台后端去噪。     

基于加权块稀疏联合非凸低秩约束的高光谱图像去条带方法

针对高光谱图像（hyperspectral images, HSI）去条带易引起影像结构细节丢失问题,提出一种基于加权块稀疏（weighted block sparsity, WBS）正则化联合最小最大非凸惩罚（minimax concave penalty, MCP）约束的HSI去条带方法。本算法采用加权?_{2, 1}范数和MCP范数对条带稀疏结构和低秩约束,?₁范数对干净图像结构保持正则化约束,构建加权块稀疏和MCP约束的条带去除模型,采用交替方向乘子（alternating direction method of multipliers, ADMM）算法迭代求解对应模型,重建获得干净的HSI图像。实验结果表明,提出方法在实际HSI的平均等效视数从28.45提高到83.47,边缘保持指数较其他算法至少增加0.056,特别是对于非周期条带噪声,采用自适应权值更新稀疏水平,增强了组稀疏性,在保持影像边缘和加强区域平滑性方面性能更佳,去噪声效果更好。     

基于C-V模型的改进快速水平集图像分割法

针对水平集方法计算复杂度高,无法满足实时系统要求的缺陷,提出一种改进的快速水平集算法。该算法对快速水平集算法进行简化,采用单链表表示轮廓曲线。利用C-V模型的二值拟合项来设计曲线演化的速度函数,保留了C-V模型的全局优化特性。还给出了一个基于单链表中轮廓点个数变化的水平集演化终止准则。该算法不仅明显提高了分割速度,且对噪声图像也能实现高效的分割。     

中阶梯光栅光谱仪谱图背景去除算法

中阶梯光栅光谱仪凭借交叉色散特性实现全谱瞬态直读,面阵探测器接收的二维光谱图像需要还原成一维谱图以提取有效波长。由于二维谱图含有庞大的数据,且有效信息仅占极小比例,因此在谱图还原前进行背景去除能够减小数据量、提高运算速度。详细分析了中阶梯光栅光谱仪二维图像的特点,并针对其特点提出了背景去除算法。将图像边缘检测方法应用于弥散光斑的检测中,选择合适的边缘检测算子与原始图像卷积得到边缘图像,设置边缘图像的全局阈值对其进行二值分割,最终利用二值边缘图像映射原始图像得到去除背景的二维谱图。依据不同元素灯在不同积分时间下所拍摄的谱图,对比不同边缘检测算法的背景去除效果,分析了各算子对算法速度、精度的影响。实验结果表明本文提出的算法运算简单、边缘图像阈值易于计算、目标提取精度高,处理后的图像可以与谱图还原算法有效对接,谱图处理速度显著提升。     

Block-based disparity estimation by partial finite ridgelet distortion search (PFRDS)

In stereo vision applications, computing the disparity map is an important issue. Performance of different approaches totally depends on the employed similarity measurements. In this paper finite ridgelet transform is used to define an edge sensitive block distortion similarity measure. Simulation results emphasize to outperform in the conventional criteria and is less sensitive to noise, especially at the edge set of images. To speed computations, a new partial search algorithm based on energy conservation property of FRIT is proposed.     

Developing a Hong Kong based speed correction factor by CPX method

A systematic procedure for developing speed correction factor to adjust measured tyre/road noise is proposed in this paper. The draft ISO/CD 11819-2 on the Close-Proximity method to measure tyre/road noise stipulates that speed correction is needed to convert the tyre/road noise level at the sampling speeds to that at the reference speeds. However, the ISO standard does not mention the exact methodology for developing the speed correction factor development, although it recommends local authorities to develop their own. In this study, the tyre/road noise and speed data over 59 runs on a high speed porous asphalt surface is recorded by our CPX trailer in Hong Kong. The instantaneous noise and speed data are then analysed using this proposed procedure and the set of speed correction factor developed is found to be more stable and repeatable.     

基于水平集的闪光照相图像分割算法

针对Chan-Vese(CV)模型局部控制能力差的缺点,将基于区域的CV模型和分割曲线的局部信息结合起来,提出了一种新的水平集图像分割算法。该算法以CV法的分割曲线为初始曲线,以获得全局收敛性,在后继分割中引入分割曲线的局部信息,以提高模型对图像中微弱信号的分割能力。对闪光照相图像的数值实验表明,该算法噪声抵抗能力强,对初始轮廓位置不敏感,能实现对含细长拓扑结构和微小孔洞的弱边界闪光图像的自动分割。