Fusion of multifocus images by lattice structures

Image fusion methods based on multiscale transform (MST) suffer from high computational load due to the use of fast Fourier transforms (ffts) in the lowpass and highpass filtering steps. Lifting wavelet scheme which is based on second generation wavelets has been proposed as a solution to this issue. Lifting Wavelet Transform (LWT) is composed of split, prediction and update operations all implemented in the spatial domain using multiplications and additions, thus computation time is highly reduced. Since image fusion performance benefits from undecimated transform, it has later been extended to Stationary Lifting Wavelet Transform (SLWT). In this paper, we propose to use the lattice filter for the MST analysis step. Lattice filter is composed of analysis and synthesis parts where simultaneous lowpass and highpass operations are performed in spatial domain with the help of additions/multiplications and delay operations, in a recursive structure which increases robustness to noise. Since the original filter is designed for the undecimated case, we have developed undecimated lattice structures, and applied them to the fusion of multifocus images. Fusion results and evaluation metrics show that the proposed method has better performance especially with noisy images while having similar computational load with LSWT based fusion method.     

A mathematical and physical Study of multiscale deconvolution models of turbulence

This work presents a rigorous analysis of mathematical and physical properties for solutions of multiscale deconvolution turbulence models. We show that solutions of these models exactly conserve model quantities for the integral invariants of fundamental physical importance: kinetic energy, helicity, and (in two dimensions) enstrophy. The kinetic energy conservation is the key that allows us to next apply the phenomenology of homogeneous, isotropic turbulence to establish the existence of a model energy cascade and, in particular, that the cascade exhibits enhanced energy dissipation in a secondary accelerated cascade, which ends at the model's microscale (which we establish is larger than the Kolmogorov microscale). We also prove that the model dissipates energy at the same rate as true turbulent flow, ～ O(U³ ∕ L), independent of Reynolds number. Lastly, we prove the existence of global attractors for the model solutions; the proof of which also shows that solutions are actually one degree of regularity higher than previously known. Copyright © 2012 John Wiley & Sons, Ltd.     

Residual‐based stabilization of the finite element approximation to the acoustic perturbation equations for low Mach number aeroacoustics

The acoustic perturbation equations (APE) are suitable to predict aerodynamic noise in the presence of a non‐uniform mean flow. As for any hybrid computational aeroacoustics approach, a first computational fluid dynamics simulation is carried out from which the mean flow characteristics and acoustic sources are obtained. In a second step, the APE are solved to get the acoustic pressure and particle velocity fields. However, resorting to the finite element method (FEM) for that purpose is not straightforward. Whereas mixed finite elements satisfying an appropriate inf–sup compatibility condition can be built in the case of no mean flow, that is, for the standard wave equation in mixed form, these are difficult to implement and their good performance is yet to be checked for more complex wave operators. As a consequence, strong simplifying assumptions are usually considered when solving the APE with FEM. It is possible to avoid them by resorting to stabilized formulations. In this work, a residual‐based stabilized FEM is presented for the APE at low Mach numbers, which allows one to deal with the APE convective and reaction terms in its full extent. The key of the approach resides in the design of the matrix of stabilization parameters. The performance of the formulation and the contributions of the different terms in the equations are tested for an acoustic pulse propagating in sheared‐solenoidal mean flow, and for the aeolian tone generated by flow past a two‐dimensional cylinder. Copyright © 2016 John Wiley & Sons, Ltd.     

Phase retrieval from fringe pattern with 1-D discrete wavelet transform

This paper proposes a novel method to obtain frequency modulation (FM) signals from a single fringe pattern for phase retrieval. First, a 1D discrete Meyer wavelet is employed to decompose the pattern image signal row by row and the soft-thresholding approach is applied to remove noise. The low frequency coefficients of the wavelet decomposition are then set to 0, and the signal is reconstructed. Moreover, the optimal wavelet decomposition level is adaptively determined using a cost function-based method. The reconstructed signal, which no longer contains a background component, is normalized using a nonlinear and piecewise normalization method. The proposed method is faster and more accurate than some other phase retrieval approaches, which is illustrated with two test cases.     

An algebraic variational multiscale–multigrid method for large-eddy simulation of turbulent variable-density flow at low Mach number

An algebraic variational multiscale–multigrid method is proposed for large-eddy simulation of turbulent variable-density flow at low Mach number. Scale-separating operators generated by level-transfer operators from plain aggregation algebraic multigrid methods enable the application of modeling terms to selected scale groups (here, the smaller of the resolved scales) in a purely algebraic way. Thus, for scale separation, no additional discretization besides the basic one is required, in contrast to earlier approaches based on geometric multigrid methods. The proposed method is thoroughly validated via three numerical test cases of increasing complexity: a Rayleigh–Taylor instability, turbulent channel flow with a heated and a cooled wall, and turbulent flow past a backward-facing step with heating. Results obtained with the algebraic variational multiscale–multigrid method are compared to results obtained with residual-based variational multiscale methods as well as reference results from direct numerical simulation, experiments and LES published elsewhere. Particularly, mean and various second-order velocity and temperature results obtained for turbulent channel flow with a heated and a cooled wall indicate the higher prediction quality achievable when adding a small-scale subgrid-viscosity term within the algebraic multigrid framework instead of residual-based terms accounting for the subgrid-scale part of the non-linear convective term.     

基于小波变换的涡喷发动机遥测数据分析方法研究

在飞行试验中,由于涡喷发动机遥测数据有限,因此要对其故障进行准确分析定位存在很大难度。利用小波变换对涡喷发动机涡轮转速、喷嘴前压力等遥测数据经过去噪提取后,找出奇异点,并经系统去噪筛选,形成数据文件,利用BP神经网络的模式分类功能,发现故障出现的时刻及部件,并结合研制过程中地面试车数据库,可实现准确定位涡喷发动机故障部位及类型。实际应用证明方法实用有效,可进一步推广到其他系统或部件的故障分析工作中。     

小波神经网络在MC-CDMA多用户检测技术中应用研究

对多载波码分多址(MC-CDMA)系统的干扰受限问题,提出了利用小波变换良好的时频局部特性和神经网络的良好自学习能力,提高多用户检测性能的原理。同时为解决网络权值和参数修正进化缓慢并且容易陷入最小的问题,采用增加动量项的方法提高网络学习效率。建立了基于小波神经网络的多用户检测器并应用于MC-CDMA系统中。用MATLAB/Simulink软件搭建仿真系统,接收端采用解相关检测(MMSEC)和正交恢复(ORC)检测算法。实验表明,基于小波神经网络的多用户检测技术在误码率(BER)性能上更接近单用户的BER性能。     

基于提升小波变换的红外图像双重滤波算法

为了有效抑制红外图像中的随机噪声,采用一种基于提升小波变换的双重滤波算法来进行处理。该算法对含有噪声的红外图像实现第1次提升小波分解,然后对获得的低频和高频分解系数再次实现提升小波变换,舍弃由低频系数经过第2次提升小波变换后获得的低频系数以及由高频系数经过第2次提升小波变换后获得的高频系数。对剩余的高频系数和低频系数分别采用改进阈值函数模型以及改进非局部均值滤波算法进行处理,在此基础上实现小波系数重构。为了改善滤波后图像视觉效果,再引入直方图均衡化算法进行处理。通过理论分析和实验验证,获得了相关的标准测试图像和红外图像测试结果以及峰值信噪比和结构相似度测试数据。结果表明,该滤算法对于高质量地去除红外图像中的噪声是有帮助的。