一种利于工程应用的快速反卷积波束形成方法

针对传统的反卷积波束形成算法在处理宽带随机信号时计算量过大的问题,给出了一种利于工程应用的快速反卷积波束形成方法。利用不同频率阵列波束图的相似性,将宽带随机信号划分成非等间距的多个窄带,并在每个窄带取一个频率点的点扩散函数(Point Spread Function, PSF)进行反卷积的近似处理,极大地提高了反卷积波束形成的计算速度。通过在波束功率谱上进行边界扩展,解决了因 Richardson-Lucy(R-L)迭代算法带来的边界模糊问题,进一步提高了计算速度。仿真和海试结果表明,该方法相对于常规波束形成具有更高的分辨力、更高的处理增益和更好的旁瓣抑制能力;相对于传统反卷积波束形成计算速度提升了 50%以上。     

Two-dimensional blind Bayesian deconvolution of medical ultrasound images

A new approach to 2-D blind deconvolution of ultrasonic images in a Bayesian framework is presented. The radio-frequency image data are modeled as a convolution of the point-spread function and the tissue function, with additive white noise. The deconvolution algorithm is derived from statistical assumptions about the tissue function, the point-spread function, and the noise. It is solved as an iterative optimization problem. In each iteration, additional constraints are applied as a projection operator to further stabilize the process. The proposed method is an extension of the homomorphic deconvolution, which is used here only to compute the initial estimate of the point-spread function. Homomorphic deconvolution is based on the assumption that the point-spread function and the tissue function lie in different bands of the cepstrum domain, which is not completely true. This limiting constraint is relaxed in the subsequent iterative deconvolution. The deconvolution is applied globally to the complete radiofrequency image data. Thus, only the global part of the point-spread function is considered. This approach, together with the need for only a few iterations, makes the deconvolution potentially useful for real-time applications. Tests on phantom and clinical images have shown that the deconvolution gives stable results of clearly higher spatial resolution and better defined tissue structures than in the input images and than the results of the homomorphic deconvolution alone.     

Defocus deblurring for catadioptric omnidirectional imaging based on spatially invariant point spread function

The problem of catadioptric omnidirectional imaging defocus blur, which is caused by lens aperture and mirror curvature, becomes more severe when high resolution sensors and large apertures are applied. In order to overcome this problem, a novel method based on computational photography is proposed. Firstly, the defocus blur of catadioptric omnidirectional imaging is analyzed to calculate the point spread function for different scene points. Then, the defocus blur kernel of omnidirectional image is confirmed to be spatially invariant when rotating the focus ring of camera lens during an image’s integration time. Lastly, the deconvolution algorithm using prior sparse derivatives is applied to obtain all-focused/sharp omnidirectional images. Experimental results demonstrate that the proposed method is effective for omnidirectional image deblurring and can be applied to most existing catadioptric omnidirectional imaging systems.     

High-resolution ultrasonic imaging using fast two-dimensional homomorphic filtering.

A new method for two-dimensional deconvolution of medical ultrasonic images is presented. The spatial resolution of the deconvolved images is much higher compared to the common images of the fundamental and second harmonic. The deconvolution also results in a more distinct speckle pattern. Unlike the most published deconvolution algorithms for ultrasonic images, the presented technique can be implemented using currently available hardware in real-time imaging, with a rate up to 50 frames per second. This makes it attractive for application in the current ultrasound scanners. The algorithm is based on two-dimensional homomorphic deconvolution with simplified assumptions about the point spread function. Broadband radio frequency image data are deconvolved instead of common fundamental harmonic data. Thus, information of both the first and second harmonics is used. The method was validated on image data recorded from a tissue-mimicking phantom and on clinical image data.     

High-resolution ultrasonic imaging using two-dimensional homomorphic filtering

A new method for two-dimensional deconvolution of medical ultrasonic images is presented. The spatial resolution of the deconvolved images is much higher compared to the common images of the fundamental and second harmonic. The deconvolution also results in a more distinct speckle pattern. Unlike the most published deconvolution algorithms for ultrasonic images, the presented technique can be implemented using currently available hardware in real-time imaging, with a rate up to 50 frames per second. This makes it attractive for application in the current ultrasound scanners. The algorithm is based on two-dimensional homomorphic deconvolution with simplified assumptions about the point spread function. Broadband radio frequency image data are deconvolved instead of common fundamental harmonic data. Thus, information of both the first and second harmonics is used. The method was validated on image data recorded from a tissue-mimicking phantom and on clinical image data.     

Improving closely spaced dim object detection through multiframe blind deconvolution of near stellar neighbourhoods

A new iterative algorithm is proposed to improve the detection of dim stellar objects that are in the neighbourhood of a bright object, using short-exposure images. This method separates data functions into the primary bright object function, the neighbourhood system function, and the background function. This approach uses the principles of the Expectation-Maximization algorithm with the Gerchberg-Saxton phase retrieval algorithm to overcome the image degradation caused by the photon counting noise from the charge-coupled devices and the turbulent atmospheric conditions. The performance of this new neighbourhood system algorithm is compared with that of the multiframe blind deconvolution algorithm, using laboratory data and computer-simulated data. This paper provides an improved technique to image closely spaced dim objects.     

欧拉弹力模型的视频运动目标自适应图切检测

传统的视频运动目标图切检测算法基于低阶马尔科夫随机场,能量函数的低阶近似无法准确描述图像像素的空间相关性,导致图切检测结果过度平滑。本文提出一种基于高阶欧拉弹力模型的图切检测算法,利用欧拉弹性模型优化目标边界曲线和修正能量函数的低阶近似。算法通过利用前一帧图像的检测结果,对当前帧图像运动目标像素点数和前景背景邻接像素对数进行卡尔曼预测,并不断自适应调整当前帧的图像模型参数,实现了视频运动目标的连续全局优化检测。实验结果验证了欧拉弹力模型在视频运动目标检测中的有效性,其检测结果能够更好地满足人的视觉效果。     

Computational imaging through chromatic aberration corrected simple lenses

Modern camera lenses become increasingly more complex to optimize the light efficiency of the optical system. Recent research has combined single lens optics with complex post-capture correction methods based on computational photography. This study further improves lens design by correcting chromatic aberrations, after which a simple image deconvolution method is sufficient to produce a high-quality image. We initially estimate the point spread function with a blind image deconvolution method. We add Gaussian regularization as kernel prior to improve the accuracy of kernel estimation. A fast non-blind deconvolution method is then performed to recover a clear image. Experimental results show that the proposed method is at par with state-of-the-art deconvolution approaches. We found a better trade-off between lens design and digital image processing than previous work.     

A general algorithm for accurate computation of field variables and its derivatives near the boundary in BEM

A general algorithm was proposed in the paper for the accurate computation of the field variables and its derivatives at domain points near the boundary in attempt to solve the so-called boundary layer effect in the boundary element method. The algorithm is based on the parameter, including modified Gauss–Tschebyscheff quadrature formula with the aid of the approximate distance function introduced, where the parameter is defined as the ratio of the minimum distance of the domain point to the boundary and the length of the boundary element. The algorithm is not only numerically stable because the singular part of the integrand serves as the weight function in the modified Gauss–Tschebyscheff quadrature formula but also independent of the kind of boundary elements. The method can be extended to the three-dimensional case with little modifications.Numerical examples of the potential problem and the elastic problem of plane strain were given by using the cubic and the quadratic boundary elements, respectively, showing the feasibility and the effectiveness of the proposed algorithm.     

有限元法和退火进化算法相结合分析结构模糊可靠性

结构的失效除了具有随机性，还应具有模糊性。本文在介绍一种修正的联合概率密度函数的基础上，采用有限元法和退火进化算法相结合来研究结构的模糊可靠度。在每一模糊失效水平下，有限元法用来计算荷载效应项，并将荷载效应项代入原联合概率密度函数形成修正的联合概率密度函数。为了解决进化算法的早熟收敛问题，采用模拟退火算法与进化算法相结合，以保证更有效地搜索到最可能失效点(设计点)。解决不存在显式极限状态方程的大部分实际结构的可靠度研究的困难。数例结果表明该法可直接应用现有的确定性的有限元程序，并且具有很好的效率和精度。     

最大相关峭度解卷积结合稀疏编码收缩的齿轮微弱故障特征提取

针对强背景噪声环境下齿轮早期故障诊断问题,提出了最大相关峭度解卷积结合稀疏编码收缩的微弱故障特征提取方法。由于最大相关峭度解卷积算法的处理结果同时受滤波器长度参数及解卷积周期参数的影响,为自适应地实现最佳的解卷积效果,利用粒子群算法优良的寻优特性,对最大相关峭度解卷积算法的最佳影响参数组合进行搜索。原故障信号经影响参数优化的最大相关峭度解卷积算法处理后,冲击特征会明显增强,为剔除剩余噪声,对所获解卷积信号做进一步稀疏编码收缩降噪处理,并通过分析降噪信号的包络谱来识别故障特征频率成分。实例分析结果验证了该方法的有效性和可靠性。     

Parallel-distributed blind deconvolution based on a self-organizing neural network

A parallel-distributed blind deconvolution method based on a self-organizing neural network is introduced. A large degraded image is segmented into smaller subpatterns. Each subpattern can be used to get a blur function. Moreover, we propose a two-step unsupervised learning method in the self-organizing neural network. The two-step learning method includes parallel learning and series learning operations. The series learning operation is similar to a typical learning operation in the self-organizing neural network. The parallel learning operation is used as a positive perturbation to let the learning operation leave a local minimum. Several improved blur functions can be estimated from the different subpatterns, and the optimized blur function is evolved by use of a genetic algorithm. As the blur function is estimated, the source image of the large degraded image can be easily restored by use of a Wiener-type filter or other deconvolution methods. Computer simulations show that the proposed parallel-distributed blind deconvolution method gives good reconstruction and that the two-step learning method in the self-organizing neural network can promote learning. Since the main computational cost is dependent on the size of the subpattern, the proposed method is effective for the restoration of the large image.     

基于盲解卷积和聚类的机械弱冲击声信号提取

针对对比函数和紧缩方法的时域盲解卷积算法在分离机械弱冲击信号时,其结果易受解卷积滤波器长度影响的缺点,提出结合分层聚类的改进算法.该算法通过设置一个变长度滤波器组,对获得的多个盲解卷积结果进行聚类分析,解决了单次盲解卷积结果不确定问题,获得了可靠性高的估计信号.计算机仿真和实际环境下故障轴承声信号提取实验验证了算法的有效性.     

风力发电机轴承复合故障特征提取方法研究

针对较强背景噪声干扰下的风力发电机轴承复合故障特征难以准确提取的问题,提出了基于自适应最大相关峭度解卷积(AMCKD)的风力发电机轴承复合故障特征提取的方法。首先,利用人工鱼群算法(AFSA)以解卷积信号包络谱的相关峭度值为目标函数对最大相关峭度解卷积算法(MCKD)的影响参数自适应寻优;然后,将影响参数优化的MCKD对原始故障信号进行解卷积处理,对解卷积信号进行包络谱分析,通过包络谱中的主导频率成分与轴承各元件的故障特征频率相对比,准确实现轴承复合故障的诊断。仿真和工程应用实例验证了所提方法的有效性与实用性。     

A restoration framework for ultrasonic tissue characterization

Ultrasonic tissue characterization has become an area of intensive research. This procedure generally relies on the analysis of the unprocessed echo signal. Because the ultrasound echo is degraded by the non-ideal system point spread function, a deconvolution step could be employed to provide an estimate of the tissue response that could then be exploited for a more accurate characterization. In medical ultrasound, deconvolution is commonly used to increase diagnostic reliability of ultrasound images by improving their contrast and resolution. Most successful algorithms address deconvolution in a maximum a posteriori estimation framework; this typically leads to the solution of l(2)-norm or (1)-norm constrained optimization problems, depending on the choice of the prior distribution. Although these techniques are sufficient to obtain relevant image visual quality improvements, the obtained reflectivity estimates are, however, not appropriate for classification purposes. In this context, we introduce in this paper a maximum a posteriori deconvolution framework expressly derived to improve tissue characterization. The algorithm overcomes limitations associated with standard techniques by using a nonstandard prior model for the tissue response. We present an evaluation of the algorithm performance using both computer simulations and tissue-mimicking phantoms. These studies reveal increased accuracy in the characterization of media with different properties. A comparison with state-of-the-art Wiener and l(1)-norm deconvolution techniques attests to the superiority of the proposed algorithm.     

Concurrent Implementation of the Optimal Incremental Approximation Method for the Adaptive and Meshless Solution of Differential Equations

The optimal incremental function approximation method is implemented for the adaptive and meshless solution of differential equations. The basis functions and associated coefficients of a series expansion representing the solution are selected optimally at each step of the algorithm according to appropriate error minimization criteria. Thus, the solution is built incrementally. In this manner, the computational technique is adaptive in nature, although a grid is neither built nor adapted in the traditional sense using a posteriori error estimates. Since the basis functions are associated with the nodes only, the method can be viewed as a meshless method. Variational principles are utilized for the definition of the objective function to be extremized in the associated optimization problems. Complicated data structures, expensive remeshing algorithms, and systems solvers are avoided. Computational efficiency is increased by using low-order local basis functions and the parallel direct search (PDS) optimization algorithm. Numerical results are reported for both a linear and a nonlinear problem associated with fluid dynamics. Challenges and opportunities regarding the use of this method are discussed.     

Noniterative blind data restoration by use of an extracted filter function

A signal-processing algorithm has been developed where a filter function is extracted from degraded data through mathematical operations. The filter function can then be used to restore much of the degraded content of the data through use of a deconvolution algorithm. This process can be performed without prior knowledge of the detection system, a technique known as blind deconvolution. The extraction process, designated self-deconvolving data reconstruction algorithm, has been used successfully to restore digitized photographs, digitized acoustic waveforms, and other forms of data. The process is noniterative, computationally efficient, and requires little user input. Implementation is straightforward, allowing inclusion into many types of signal-processing software and hardware. The novelty of the invention is the application of a power law and smoothing function to the degraded data in frequency space. Two methods for determining the value of the power law are discussed. The first method assumes the power law is frequency dependent. The function derived comparing the frequency spectrum of the degraded data with the spectrum of a signal with the desired frequency response. The second method assumes this function is a constant of frequency. This approach requires little knowledge of the original data or the degradation.     

Iterative ultrasonic signal and image deconvolution for estimation of the complex medium response

The ill‐conditioned inverse problem of estimating ultrasonic medium responses by deconvolution of RF signals is investigated. The primary difference between the proposed method and others is that the medium response function is assumed to be complex‐valued rather than restricted to being real‐valued. Derived from the complex medium model, complex Wiener filtering is presented, and a Hilbert transform related limitation to inverse filtering type methods is discussed. We introduce a nonparametric iterative algorithm, the least squares method with point count regularization (LSPC). The algorithm is successfully applied to simulated and experimental data and demonstrates the capability of recovering both the real and imaginary parts of the medium response. The simulation results indicate that the LSPC method can outperform Wiener filters and improve the resolution of the ultrasound system by factors as high as 3.7. Experimental results using a single element transducer and a conventional medical ultrasound system with a linear array transducer show that despite the errors in pulse estimation and the noise in the RF signals, excellent results can be obtained, demonstrating the stability and robustness of the algorithm. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 266–277, 2005     

基于改进遗传算法的反卷积信号识别

信号处理中的反卷积是一个不适定问题，在泛涵理论上求取反卷积正则解的关键足求距离的最小。遗传算法在优化方面具有优势，因此提出用遗传算法优化求取最小值进行反卷积信号诊断。但足由于传统的遗传算法存在着一些问题，易陷入局部极小点导致成熟前收敛，使得反卷积问题的解决有误差，恢复的波形具有波动性，精度还不够，由此我们对传统的遗传算法进行了改进，改进后模拟计算发现恢复的信号波形精度明显上升，和原信号波形很相象，比较准确地反映了原信号固有的特性。     

基于解析模态分解和希尔伯特变换的模态参数辨识新方法

针对航天器结构低频、密频的模态参数辨识问题,提出一种将解析模态分解(AMD)与希尔伯特变换(HT)相结合的模态辨识方法(AMD+HT),根据结构上任意一点的脉冲响应信号,对系统结构的频率和模态阻尼比进行参数识别。以箱型卫星模型为例,分别对固定状态下卫星帆板和卫星整体结构的低阶模态进行模态辨识,并与LMS数据采集系统分析结果和ANSYS有限元仿真结果对比,验证了该方法对低频、密频结构模态辨识的正确性和优越性。