一种改进的叠前逆时偏移方法

叠前逆时偏移利用双程波动方程,基于时间正推震源波场和反推检波点波场,理论上无倾角限制,可以实现对回转波、棱镜波以及多次波的正确成像.从伴随算子理论角度考虑对伴随叠前逆时偏移算法进行了改进,即从最大时刻开始反推检波点波场时,在每个时间步长,取出对应时刻的震源波场快照取代对应时刻的记录数据叠加到反推的检波点波场中,其它步骤完全相同.该算法在不增加计算量与存储的情况下可以提高深层复杂构造成像质量,为避免存储和降低低频噪声提出的各种改进方案都可以应用到方法中.数值试算结果表明方法有效可行.     

古河道指导下的断层复杂区储层预测方法——以齐家南地区高台子油层为例

由河道砂体形成的岩性油气藏在油气勘探中占有很重要的地位.随着时间的推移,古河道无论是宽度,还是流经路线都在不断的被改造,导致河道的复杂性增强.多期河道错断切叠,造成河道地震响应特征模糊,相关储层预测难度大.特别是在断裂系统发育的地区,断层对成像的干扰以及对河道的改造使得储层的预测变得更加困难.为解决这一问题,首先利用倾向曲率地震属性对断层进行识别;在此基础上,利用瞬时频率等优选出的地震属性对古河道进行识别;最后对优选的地震属性进行神经网络属性融合,利用融合结果对断层复杂区储层进行预测.通过井的验证,证明先识别断层,排除其对古河道的干扰,再进行河道砂体预测的方法可以有效减小复杂断层对储层预测的干扰,提高储层预测精度.     

基于字典学习的地震数据随机噪声压制算法

针对传统变换基函数难以获得地震数据最优的稀疏表示,提出基于字典学习的随机噪声压制算法,将地震数据分块,每一块包含多个地震记录道在一定采样时间段内波形的信息,利用自适应字典学习技术,以地震数据块为训练样本,根据地震数据邻近块中记录道相似的特点,构造超完备字典,稀疏编码地震数据,从而恢复数据的主要特征,压制随机噪声.实验表明算法具有较高的PSNR值,并且能较好的保持地震数据纹理复杂区域的局部特征.     

基于主动轮廓模型的运动线虫中心线定位算法

通过光学成像手段研究秀丽隐杆线虫(以下简称线虫)感知行为与对应神经系统中神经元及神经环路之间的反应和调控机制有重要的科学意义.开展上述研究需要对自由无约束运动下的线虫感兴趣区域(包含特定神经元/环路的区域)进行凝视追踪成像.为此,本文开展线虫中心曲线定位算法研究,由此建立线虫活动坐标系.现有的线虫中心曲线定位算法是基于骨架化直接计算其中心曲线,然而在实际成像中,线虫身体严重扭曲会造成自身粘连甚至成卷,此外多个线虫之间、线虫与环境物体之间也会出现粘连,这都将改变线虫身体图像的拓扑结构,从而导致中心曲线定位算法失效.本文提出基于主动轮廓模型(active contour model)的中心曲线优化迭代求解方法.不同于通过线虫区域计算线虫中心曲线的直接求解思路,本文提出的方法通过预估的中心曲线来构造当前的线虫区域,再利用成像系统采集的真实图像进行修正,并对预估的中心曲线加以平滑性和均匀性等正则项约束,由此建立能量模型并转化为迭代求解能量函数的最小化问题.本文推导离散形式下的目标函数表达式,给出构造线虫区域的方法,并选择动态规划方法迭代求解能量最小问题.本文通过计算大量实际数据,验证新算法的可行性和有效性.特别是在线虫图像发生不同粘连的复杂情形下,新方法依然能够准确提取线虫中心曲线.     

KMP算法的一种新的简化算法

通过对字符串模式匹配算法BF与KMP的分析,提出了一种简化KMP算法的方法,构造了一种新的计算next函数的方法,简化后的算法比KMP更清晰直观.经过复杂性分析和上机实验,得出当模式串的长度不大时,简化算法是一种高效的模式匹配算法.     

基于Grbner基方法的交通分配算法

交通规划中的第四阶段交通分配是交通规划中最重要的环节之一,合理的交通分配方法是未来规划期内交通运输系统状态良好的关键,对交通分配模型进行优化有利于交通规划正确高效.经典的交通规划分配模型算法计算复杂,比较次数多,计算量大,而Grbner基方法在计算机上容易实现,计算思路清晰简洁,适合在交通分配中采用.选取了交通分配中的典型算法增量分配法,对其中最短路算法用Grbner基方法改进,构造了基于Grbner基方法的交通分配模型.模型先将交通分配中的最短路问题转化为求多项式集的Grbner基,然后直接得出交通分配中的最短路径,使交通分配算法高效简洁.最后,为算法加以实例佐证,证实算法在工程应用中可行.     

一类凸优化的加速混合下降算法

凸优化问题的混合下降算法利用近似条件的已知信息和随机数扩张预测校正步得到了一组下降方向.而前向加速收缩算法利用高斯赛德尔迭代算法的技术,结合邻近点算法和近似邻近点算法的思想,构造了富有扩张性的下降方向.本文借鉴混合下降算法和前向加速收缩算法的思想,利用已有近似规则信息改善了混合下降算法的下降方向,得到了一类凸优化问题的加速混合下降算法.随后利用Markov不等式、凸函数性质和投影的基本性质等,实现了算法的依概率收敛证明.一系列数值试验表明了加速混合下降算法的有效性和效率性.     

生理药动学模型参数估计问题

根据生理药动学模型的特点,把非线性药动学模型转化为线性模型,并验证线性模型的精确性.在此基础上,构造估计药动学模型参数的目标函数,并利用非线性优化算法求解模型参数.仿真结果表明,我们的算法具有快速、精确和稳定的特点.给出了一种快速估计复杂生理药动学模型参数的方法,这为解决复杂生理药动学模型的参数估计问题提供了一种有效工具.     

α尺度r重正交共轭滤波器的构造

对于α尺度r重紧支撑正交多小波系统,给出了由长为L的α尺度r重正交共轭滤波器构造长为L+1的α尺度r重正交共轭滤波器的一般方法,也给出了由低阶矩阵滤波器构造高阶矩阵滤波器的方法.若给定的正交共轭滤波器满足完全重构条件,则利用算法构造新的滤波器也满足完全重构条件,算法还保持正交共轭滤波器对称性,这一点在信号处理方面具有很好的应用价值.     

具有高消失矩的3-进双正交对称小波的构造

本文用矩阵对称扩充来构造了具有高消失矩的3带对称双正交小波.利用矩阵扩充,获得了3维矩阵对称扩充方法和小波构造的算法,并且,该算法便于计算机程序化实现;利用两个实例验证了相关的结论.     

Image segmentation by level set evolution with region consistency constraint

Image segmentation is a key and fundamental problem in image processing, computer graphics, and computer vision. Level set based method for image segmentation is used widely for its topology flexibility and proper mathematical formulation. However, poor performance of existing level set models on noisy images and weak boundary limit its application in image segmentation. In this paper, we present a region consistency constraint term to measure the regional consistency on both sides of the boundary, this term defines the boundary of the image within a range, and hence increases the stability of the level set model. The term can make existing level set models significantly improve the efficiency of the algorithms on segmenting images with noise and weak boundary. Furthermore, this constraint term can make edge-based level set model overcome the defect of sensitivity to the initial contour. The experimental results show that our algorithm is efficient for image segmentation and outperform the existing state-of-art methods regarding images with noise and weak boundary.     

A fast alternating minimization algorithm for total variation deblurring without boundary artifacts

Recently, a fast alternating minimization algorithm for total variation image deblurring (FTVd) has been presented by Wang, Yang, Yin, and Zhang (2008) [32]. The method in a nutshell consists of a discrete Fourier transform-based alternating minimization algorithm with periodic boundary conditions and in which two fast Fourier transforms (FFTs) are required per iteration. In this paper, we propose an alternating minimization algorithm for the continuous version of the total variation image deblurring problem. We establish convergence of the proposed continuous alternating minimization algorithm. The continuous setting is very useful to have a unifying representation of the algorithm, independently of the discrete approximation of the deconvolution problem, in particular concerning the strategies for dealing with boundary artifacts. Indeed, an accurate restoration of blurred and noisy images requires a proper treatment of the boundary. A discrete version of our continuous alternating minimization algorithm is obtained following two different strategies: the imposition of appropriate boundary conditions and the enlargement of the domain. The first one is computationally useful in the case of a symmetric blur, while the second one can be efficiently applied for a nonsymmetric blur. Numerical tests show that our algorithm generates higher quality images in comparable running times with respect to the Fast Total Variation deconvolution algorithm.     

A framework for advancing front techniques of finite element mesh generation

Advancing front techniques are a family of methods for finite element mesh generation that are particularly effective in dealing with complicated boundary geometries. In the first part of this paper, conditions are presented which ensure that any planar aft algorithm that meets these conditions terminates in a finite number of steps with a valid triangulation of the input domain. These conditions are described by specifying a framework of subtasks that can accommodate many aft methods and by prescribing the minimal requirements on each subtask that ensure correctness of an algorithm that conforms to the framework.An important efficiency factor in implementing an aft is the data structure used to represent the unmeshed regions during the execution of the algorithm. In the second part of the paper, we discuss the use of the constrained Delaunay triangulation as an efficient abstract data structure for the unmeshed regions. We indicate how the correctness conditions of the first part of the paper can be met using this representation. In this case, we also discuss the additional requirements on the framework which ensure that the generated mesh is a constrained Delaunay triangulation for the original boundary.The first author has been supported by CERFACS, Toulouse, France. Support was provided to the second author by the Natural Sciences and Engineering Research Council of Canada, and by the Information Technology Research Centre of Ontario.     

Synthetic boundary conditions for image deblurring

In this paper we introduce a new boundary condition that can be used when reconstructing an image from observed blurred and noisy data. Our approach uses information from the observed image to enforce boundary conditions that continue image features such as edges and texture across the boundary. Because of its similarity to methods used in texture synthesis, we call our approach synthetic boundary conditions. We provide an efficient algorithm for implementing the new boundary condition, and provide a linear algebraic framework for the approach that puts it in the context of more classical and well known image boundary conditions, including zero, periodic, reflective, and anti-reflective. Extensive numerical experiments show that our new synthetic boundary conditions provide a more accurate approximation of the true image scene outside the image boundary, and thus allow for better reconstructions of the unknown, true image scene.     

Kronecker product approximations for image restoration with anti‐reflective boundary conditions

The anti‐reflective boundary condition for image restoration was recently introduced as a mathematically desirable alternative to other boundary conditions presently represented in the literature. It has been shown that, given a centrally symmetric point spread function (PSF), this boundary condition gives rise to a structured blurring matrix, a submatrix of which can be diagonalized by the discrete sine transform (DST), leading to an O(n² log n) solution algorithm for an image of size n × n. In this paper, we obtain a Kronecker product approximation of the general structured blurring matrix that arises under this boundary condition, regardless of symmetry properties of the PSF. We then demonstrate the usefulness and efficiency of our approximation in an SVD‐based restoration algorithm, the computational cost of which would otherwise be prohibitive. Copyright © 2005 John Wiley & Sons, Ltd.     

Adaptive cell-centered finite volume method for diffusion equations on a consistent quadtree grid

Models applied in image processing are often described by nonlinear PDEs in which a good approximation of gradient plays an important role especially in such cases where irregular finite volume grids are used. In image processing, such a situation can occur during a coarsening based on quadtree grids. We present a construction of a deformed quadtree grid in which the connection of representative points of two adjacent finite volumes is perpendicular to their common boundary enabling us to apply the classical finite volume methods. On the other hand, for such an adjusted grid, the intersection of representative points connection with a finite volume boundary is not a middle point of their common edge and standard methods cannot achieve a good accuracy. In this paper we present a new cell-centered finite volume method to evaluate solution gradients, which results into a solution of a simple linear algebraic system and we prove its unique solvability. Finally we present numerical experiments for the regularized Perona-Malik model in which we applied this new method.     

Noisy image segmentation based on nonlinear diffusion equation model

This paper addresses the segmentation problem in noisy image based on nonlinear diffusion equation model and proposes a new adaptive segmentation model based on gray-level image segmentation model. This model also can be extended to the vector value image segmentation. By virtue of the prior information of regions and boundary of image, a framework is established to construct different segmentation models using different probability density functions. A segmentation model exploiting Gauss probability density function is given in this paper. An efficient and unconditional stable algorithm based on locally one-dimensional (LOD) scheme is developed and it is used to segment the gray image and the vector values image. Comparing with existing classical models, the proposed approach gives the best performance.     

A novel image encryption algorithm using chaos and reversible cellular automata

In this paper, a novel image encryption scheme is proposed based on reversible cellular automata (RCA) combining chaos. In this algorithm, an intertwining logistic map with complex behavior and periodic boundary reversible cellular automata are used. We split each pixel of image into units of 4 bits, then adopt pseudorandom key stream generated by the intertwining logistic map to permute these units in confusion stage. And in diffusion stage, two-dimensional reversible cellular automata which are discrete dynamical systems are applied to iterate many rounds to achieve diffusion on bit-level, in which we only consider the higher 4 bits in a pixel because the higher 4 bits carry almost the information of an image. Theoretical analysis and experimental results demonstrate the proposed algorithm achieves a high security level and processes good performance against common attacks like differential attack and statistical attack. This algorithm belongs to the class of symmetric systems.     

A parametrix for the linear elastic equation with diffractive boundary and its application

In this paper, we construct parametrices near diffractive points for the boundary value problems for the linear elastic equation with free boundary condition or Dirichlet boundary condition. Naturally, our construction is similar to that for the wave equation case. However, since the linear elastic equation is a second order system, our method is more complicated. As an application to the existence of the parametrices, we prove the theorem on propagation of singularities for solutions of the boundary value problem.     

A cryptosystem based on elementary cellular automata

Based on elementary cellular automata, a new image encryption algorithm is proposed in this paper. In this algorithm, a special kind of periodic boundary cellular automata with unity attractors is used. From the viewpoint of security, the number of cellular automata attractor states are changed with respect to the encrypted image, and different key streams are used to encrypt different plain images. The cellular neural network with chaotic properties is used as the generator of a pseudo-random key stream. Theoretical analysis and experimental results have both confirmed that the proposed algorithm possesses high security level and good performances against differential and statistical attacks. The comparison with other existing schemes is given, which shows the superiority of the proposal scheme.