使用柯西分布点扩散函数模型的单幅散焦图像深度恢复

目的 当前,大多数单幅散焦图像的3维(3D)场景深度恢复方法,通常使用高斯分布描述点扩散函数(PSF)模型,依据图像边缘散焦模糊量与场景深度的对应关系获得稀疏深度图,采用不同的扩展方法得到整个场景图像的全深度图.鉴于现有方法的深度恢复结果还不够精准,对各种噪声干扰还不够健壮,提出一种基于柯西分布的点扩散函数模型计算物体图像边缘散焦模糊量的方法.方法 将输入的单幅散焦图像分别用两个柯西分布重新模糊,利用图像边缘两次重新模糊图像间梯度比值和两个柯西分布的尺度参数,可以计算出图像中边缘处的散焦模糊量.使用matting内插方法将边缘模糊量扩展到整个图像,即可恢复场景的全深度图.结果 将原始Lenna图像旋转并加入高斯噪声以模拟图像噪声和边缘位置误差,用原图与噪声图比较了柯西分布图像梯度比值与高斯分布图像梯度比值的平均误差.使用多种真实场景图像数据,将本文方法与现有的多种单幅散焦图像深度恢复方法进行了比较.柯西分布图像梯度比值的平均误差要小于高斯分布图像梯度比值的平均误差.本文方法能够从非标定单幅散焦图像中较好地恢复场景深度,对图像噪声、不准确边缘位置和邻近边缘具有更好的抗干扰能力.结论 本文方法可以生成优于现有基于高斯模型等方法的场景深度图.同时,也证明了使用非高斯模型建模PSF的可行性和有效性.     

基于频谱预处理与改进霍夫变换的离焦模糊盲复原算法

为了提高离焦模糊图像复原清晰度,提出一种基于频谱预处理与改进霍夫变换的 离焦模糊盲复原算法。首先改进模糊图像频谱预处理策略,降低了噪声对零点暗圆检测的影响。 然后改进霍夫变换圆检测算法,在降低算法复杂度的同时,增强了模糊半径估计的准确性。最 后利用混合特性正则化复原图像模型对模糊图像进行迭代复原,使复原图像的边缘细节更加清 晰。实验结果表明,提出的模糊半径估计方法较其他方法平均误差更小,改进的频谱预处理策 略更有利于零点暗圆检测,改进的霍夫变换圆检测算法模糊半径估计精度更高,所提算法对已 知相机失焦的小型无人机拍摄的离焦模糊图像具有更好的复原效果。针对离焦模糊图像复原, 通过理论分析和实验验证了改进的模糊半径估计方法的鲁棒性强,所提算法的复原效果较好。     

盲源分离与高分辨融合的DOA估计与信号恢复方法

目标方位估计(Direction of arrival, DOA)和信号恢复分别是水下目标定位、跟踪与识别的前提.基于盲源分离方法可以得到含有阵列流形信息的解混矩阵, 融合成熟的高分辨方法提出了一种新的方位估计、信号恢复模型和方法. 在宽带信号背景下进行了仿真实验, 结果表明该方法可实现目标方位的实时估计和目标信号的恢复. 在同等条件下完成同样的目标方位分辨率, 比单纯的高分辨方法要求的阵元数和快拍数较少, 要求的信噪比要低. 海上实测数据检验也表明, 比常规的最小方差无失真响应(Minimum variance distortionless response, MVDR)方法得到了更好的结果, 明显提高了弱目标信号的空间谱能量, 增强了检测弱目标信号的能力.     

基于维纳滤波和快速独立分量分析的有噪混合图像盲分离

简要介绍了独立分量分析的基本数学模型和算法,在此基础上探讨了独立分量分析在有噪混合图像分离中的应用,提出了一种结合维纳滤波去噪与独立分量分析相结合的解决方法.实验结果表明,该方法能有效地降低噪声信号的影响,较好地恢复了原始图像.     

基于级联结构的航空发动机振动信号盲分离

级联型的盲抽取紧缩方法具有算法快速、冗余性小、单元结构简单和易于实现等优点,在盲信号处理领域有着广泛的应用.针对该结构在航空发动机振动信号盲分离中的应用进行了研究,首先介绍了这种级联结构的可行性和优越性以及抽取紧缩单元的优化算法,然后对某型航空发动机实测的故障振动信号利用级联盲抽取结构进行振源分离,并通过对分离出的振源...     

Regularization Methods for Blind Deconvolution and Blind Source Separation Problems

This paper is devoted to blind deconvolution and blind separation problems. Blind deconvolution is the identification of a point spread function and an input signal from an observation of their convolution. Blind source separation is the recovery of a vector of input signals from a vector of observed signals, which are mixed by a linear (unknown) operator. We show that both problems are paradigms of nonlinear ill-posed problems. Consequently, regularization techniques have to be used for stable numerical reconstructions. In this paper we develop a rigorous convergence analysis for regularization techniques for the solution of blind deconvolution and blind separation problems. Convergence of regularized point spread functions and signals to a solution is established and a convergence rate result in dependence of the noise level is presented. Moreover, we prove convergence of the alternating minimization algorithm for the numerical solution of regularized blind deconvolution problems and present some numerical examples. Moreover, we show that many neural network approaches for blind inversion can be considered in the framework of regularization theory. Date received: August 17, 1999. Date revised: September 1, 2000.     

Blind Source Recovery in a State-Space Famework: Algorithms for Static and Dynamic Environments

This paper describes a generalized state space Blind Source Recovery (BSR) framework obtained by using the Kullback-Lieblar divergence as a performance functional and the application of optimization theory under the constraints of a feedforward state space structure. Update laws for both the non-linear and the linear dynamical systems have been derived for the domain of dynamic blind source recovery along both ordinary stochastic gradient and the Riemannian contra-variant gradient directions. The choice of the rich state space demixing network structure allows for the development of potent learning rules, capable of handling most filtering paradigms and convenient extension to non-linear models. Some particular filtering cases are subsequently derived from this general structure and are compared with material in the recent literature. Some of this reported work has also been implemented in dedicated hardware/software. An illustrative simulation example has been presented to demonstrate the online adaptation capabilities of the proposed algorithms.     

A Survey of Urban Reconstruction

This paper provides a comprehensive overview of urban reconstruction. While there exists a considerable body of literature, this topic is still under active research. The work reviewed in this survey stems from the following three research communities: computer graphics, computer vision and photogrammetry and remote sensing. Our goal is to provide a survey that will help researchers to better position their own work in the context of existing solutions, and to help newcomers and practitioners in computer graphics to quickly gain an overview of this vast field. Further, we would like to bring the mentioned research communities to even more interdisciplinary work, since the reconstruction problem itself is by far not solved.