Electronic Noise Modeling in Statistical Iterative Reconstruction

We consider electronic noise modeling in tomographic image reconstruction when the measured signal is the sum of a Gaussian distributed electronic noise component and another random variable whose log-likelihood function satisfies a certain linearity condition. Examples of such likelihood functions include the Poisson distribution and an exponential dispersion (ED) model that can approximate the signal statistics in integration mode X-ray detectors. We formulate the image reconstruction problem as a maximum-likelihood estimation problem. Using an expectation-maximization approach, we demonstrate that a reconstruction algorithm can be obtained following a simple substitution rule from the one previously derived without electronic noise considerations. To illustrate the applicability of the substitution rule, we present examples of a fully iterative reconstruction algorithm and a sinogram smoothing algorithm both in transmission CT reconstruction when the measured signal contains additive electronic noise. Our simulation studies show the potential usefulness of accurate electronic noise modeling in low-dose CT applications.      

基于改进剪切波和Canny的故障区域检测算法研究

针对机械故障红外热图像存在各种噪声造成故障区域测量参数提取精度低的问题,本文提出了基于改进剪切波和Canny的故障区域检测算法。该算法包括基于改进剪切波的去噪算法和改进Canny算子的边缘检测算法,在去噪中本文提出的是一种基于剪切波和高阶谱相结合的新算法,可以有效地去除高斯噪声与椒盐噪声的混合噪声并保留图像细节;在图像的边缘检测中本文利用剪切波与Canny算子相结合的改进新算法对红外故障图像进行边缘提取,从而消除了传统的Canny算子在检测时出现的伪边缘现象。实验仿真结果表明本文提出的去噪算法与传统的去噪算法相比显著提高了图像的峰值信噪比(PSNR),同时图像的边缘和细节也得到了很好的保留,实现了故障区域特征参数的精确有效提取。     

Wave Analysis of Noise in Interconnected Multiport Networks

The noise performance of multiport networks of arbitrary topology is treated using wave analysis.This approach has advantages over other methods when using computer-aided design programs that are based on scattering parameters. In this paper we discuss the wave representation of noise in two-ports and passive multiports. We indicate bow to compute the noise performance of an arbitrary network and we demonstrate the effectiveness of this approach with an example.     

Sonar image segmentation using an unsupervised hierarchical MRFmodel

This paper is concerned with hierarchical Markov random field (MRP) models and their application to sonar image segmentation. We present an original hierarchical segmentation procedure devoted to images given by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the sea-bed) and sea-bottom reverberation. The proposed unsupervised scheme takes into account the variety of the laws in the distribution mixture of a sonar image, and it estimates both the parameters of noise distributions and the parameters of the Markovian prior. For the estimation step, we use an iterative technique which combines a maximum likelihood approach (for noise model parameters) with a least-squares method (for MRF-based prior). In order to model more precisely the local and global characteristics of image content at different scales, we introduce a hierarchical model involving a pyramidal label field. It combines coarse-to-fine causal interactions with a spatial neighborhood structure. This new method of segmentation, called the scale causal multigrid (SCM) algorithm, has been successfully applied to real sonar images and seems to be well suited to the segmentation of very noisy images. The experiments reported in this paper demonstrate that the discussed method performs better than other hierarchical schemes for sonar image segmentation.     

Texture-preserving deconvolution via image decomposition

The image with rich textures can be decomposed into the sum of a geometric part and a textural part. Inspired by this fact, we propose an efficient texture-preserving image deconvolution algorithm based on image decomposition. Our algorithm restores the geometric part and textural part, respectively, by incorporating \(L_0\) gradient minimization and a wave atoms-based Wiener shrinkage filter. The \(L_0\)-based gradient minimization method could globally locate important edges, main structures. The wave atoms transform offers a better representation of images containing oscillatory patterns and textures than other known transforms. Our method contains three steps for restoring texture images. First, we propose an image deconvolution method based on \(L_0\) gradient minimization to restore geometric part of the image with minimal loss of image detail components. Next, we use a Wiener shrinkage filter in the wave atom domain to attenuate the leaked colored noise and extract fine details. Finally, we obtain the estimated image by adding the two image parts together. We compare our deconvolution algorithm with other competitive deconvolution techniques in terms of ISNR, SSIM, and visual quality.     

结合频率估计的InSAR区域增长相位解缠方法

提出一种结合频率估计的InSAR区域增长相位解缠方法。利用最大似然频率估计方法,估计出局部频率和已展开的相位共同线性,预测出局部区域中心点的展开相位,整体上采用区域生长策略,在质量图的引导下按照从高质量区域到低质量区域的顺序进行积分,可以有效地减小误差传递,较大程度地提高了相位展开结果的精度。数据实验结果验证了这种合成算法的有效性。     

Spatially adaptive wavelet-based multiscale image restoration

In this paper, we present a new spatially adaptive approach to the restoration of noisy blurred images, which is particularly effective at producing sharp deconvolution while suppressing the noise in the flat regions of an image. This is accomplished through a multiscale Kalman smoothing filter applied to a prefiltered observed image in the discrete, separable, 2-D wavelet domain. The prefiltering step involves constrained least-squares filtering based on optimal choices for the regularization parameter. This leads to a reduction in the support of the required state vectors of the multiscale restoration filter in the wavelet domain and improvement in the computational efficiency of the multiscale filter. The proposed method has the benefit that the majority of the regularization, or noise suppression, of the restoration is accomplished by the efficient multiscale filtering of wavelet detail coefficients ordered on quadtrees. Not only does this lead to potential parallel implementation schemes, but it permits adaptivity to the local edge information in the image. In particular, this method changes filter parameters depending on scale, local signal-to-noise ratio (SNR), and orientation. Because the wavelet detail coefficients are a manifestation of the multiscale edge information in an image, this algorithm may be viewed as an "edge-adaptive" multiscale restoration approach.     

应用改进自蛇模型和L-R算法恢复毫米波图像

在毫米波的图像恢复中,L-R算法是一种简单而有效的非线性方法,但当噪声不可忽略时,L-R算法难以获得较好的复原结果。针对毫米波图像数据量少和图像分辨率低的特点,提出基于改进自蛇模型和L-R算法毫米波图像恢复方法,以局部方差构造自蛇模型的边缘停止函数,其改进自蛇模型在消除噪声的同时更能够保留图像中的边缘和细节特征,然后使用L-R算法进行图像恢复,这种改进算法通过使用基于改进自蛇模型去噪能有效地减少噪声对L-R算法的影响。实验结果表明:在信噪比和相关度方面本文算法提高了L-R算法的性能,可用于含噪声的图像复原。     

Robust 3-D modeling of vasculature imagery using superellipsoids

This paper presents methods to model complex vasculature in three-dimensional (3-D) images using cylindroidal superellipsoids, along with robust estimation and detection algorithms for automated image analysis. This model offers an explicit, low-order parameterization, enabling joint estimation of boundary, centerlines, and local pose. It provides a geometric framework for directed vessel traversal, and extraction of topological information like branch point locations and connectivity. M-estimators provide robust region-based statistics that are used to drive the superellipsoid toward a vessel boundary. A robust likelihood ratio test is used to differentiate between noise, artifacts, and other complex unmodeled structures, thereby verifying the model estimate. The proposed methodology behaves well across scale-space, shows a high degree of insensitivity to adjacent structures and implicitly handles branching. When evaluated on synthetic imagery mimicking specific structural complexities in tumor microvasculature, it consistently produces ubvoxel accuracy estimates of centerlines and widths in the presence of closely-adjacent vessels, branch points, and noise. An edit-based validation demonstrated a precision level of 96.6% at a recall level of 95.4%. Overall, it is robust enough for large-scale application.     

Template matching based object recognition with unknown geometric parameters

We examine the problem of locating an object in an image when size and rotation are unknown. Previous work has shown that with known geometric parameters, an image restoration method can be useful by estimating a delta function at the object location. When the geometric parameters are unknown, this method becomes impractical because the likelihood surface to be minimized across size and rotation has numerous local minima and areas of zero gradient. We propose a new approach where a smooth approximation of the template is used to minimize a well-behaved likelihood surface. A coarse-to-fine approximation of the original template using a diffusion-like equation is used to create a library of templates. Using this library, we can successively perform minimizations which are locally well-behaved. As detail is added to the template, the likelihood surface gains local minima, but previous estimates place us within a well-behaved "bowl" around the global minimum, leading to an accurate estimate. Numerical experiments are shown which verify the value of this approach for a wide range of values of the geometric parameters.     

一种基于小波相位信息的低强度X射线影像系统的图像去噪方法

新型低强度X射线影像系统主要是由平板式单近贴静电聚焦X射线像增强器和CCD数据采集系统构成.文章简述了低强度X射线影像系统的图像噪声来源和特点,并根据图像噪声的特点,先进行多帧叠加平均预处理,再进行小波变换滤波.区别于传统的小波变换方法,引入小波变换的相位信息概念,根据噪声和图像信息小波变换后的相位不同特点,从局部和相邻尺度两方面联合进行噪声自动判别和滤除.小波反变换后,得到输出图像,通过对峰值信噪比的计算,表明该方法能取得较好效果.     

Spatially Adaptive Estimation via Fitted Local Likelihood Techniques

This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modeling of observations and estimated signals. The approach is based on the assumption of a local homogeneity of the signal: for every point there exists a neighborhood in which the signal can be well approximated by a constant. The fitted local likelihood statistics are used for selection of an adaptive size and shape of this neighborhood. The algorithm is developed for a quite general class of observations subject to the exponential distribution. The estimated signal can be uni- and multivariable. We demonstrate a good performance of the new algorithm for image denoising and compare the new method versus the intersection of confidence interval (ICI) technique that also exploits a selection of an adaptive neighborhood for estimation.     

Estimation of images and nonrigid deformations in gated emission CT

In this paper, we propose and test a new iterative algorithm to simultaneously estimate the nonrigid motion vector fields and the emission images for a complete cardiac cycle in gated cardiac emission tomography. We model the myocardium as an elastic material whose motion does not generate large amounts of strain. As a result, our method is based on minimizing an objective function consisting of the negative logarithm of a maximum likelihood image reconstruction term, the standard biomechanical model of strain energy, and an image matching term that ensures a measure of agreement of intensities between frames. Simulations are obtained using data for the four-dimensional (4-D) NCAT phantom. The data models realistic noise levels in a typical gated myocardial perfusion SPECT study. We show that our simultaneous algorithm produces images with improved spatial resolution characteristics and noise properties compared with those obtained from postsmoothed 4-D maximum likelihood methods. The simulations also demonstrate improved motion estimates over motion estimation using independently reconstructed images.     

红外热波无损检测图像噪声分析与抑制

分析了红外热波检测图像中噪声生成机理,针对图像的噪声去除增强问题,提出了一种改进的非线性偏微分方程的热波检测图像去噪增强方法。对偏微分方程在图像处理应用中的理论基础进行研究,针对现有的偏微分方程模型在图像去噪中存在的问题,并结合热波图像的特点,改进相应的偏微分方程数学模型,在此基础上对获得原始热图进行编程处理,试验结果表明：经过处理后的图像信噪比提高,对比度得到了改善,为后续的缺陷提取及损伤识别奠定了基础。     

Fuzzy local Gaussian mixture model for brain MR image segmentation

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.     

LDPC编码在OFDM水声通信中的应用与实现

本文重点关注低密度校验码（LDPC）理论在水声通信中具体实现方法。通过分析水池实验数据,讨论了水池信道普遍错误类型和应对方法,并以高斯分布为基准,分析了针对水池信道的译码初始化算法的选择和优化。     

Marked point process in image analysis

In this article, we consider the marked point process framework for image analysis. We first show that marked point processes are more adapted than Markov random fields (MRFs) including some geometrical constraints in the solution and dealing with strongly correlated noise. Then, we consider three applications in remote sensing: road network extraction, building extraction, and image segmentation. For each of them, we define a prior model, incorporating geometrical constraints on the solution. We also derive a reversible jump Monte Carlo Markov chains (RJMCMC) algorithm to obtain the optimal solution with respect to the defined models. Results show that this approach is promising and can be applied to a broad range of image processing problems.     

Extraction of signatures from check background based on afiliformity criterion

Extracting a signature from a check with a patterned background is a thorny problem in image segmentation. Methods based on threshold techniques often necessitate meticulous postprocessing in order to correctly capture the handwritten information. In this study, we tackle the problem of extracting handwritten information by means of an intuitive approach that is close to human visual perception, defining a topological criterion specific to handwritten lines which we call filiformity. This approach was inspired by the existence in the human eye of cells whose specialized task is the extraction of lines. First, we define two topological measures of filiformity for binary objects. Next, we extend these measures to include gray-level images. One of these measures, which is particularly interesting, differentiates the contour lines of objects from the handwritten lines we are trying to isolate. The local value provided by this measure is then processed by global thresholding, taking into account information about the whole image. This processing step ends with a simple fast algorithm. Evaluation of the extraction algorithm carried out on 540 checks with 16 different background patterns demonstrates the robustness of the algorithm, particularly when the background depicts a scene.     

A penalized-likelihood image reconstruction method for emission tomography,compared to postsmoothed maximum-likelihood with matched spatial resolution

Regularization is desirable for image reconstruction in emission tomography. A powerful regularization method is the penalized-likelihood (PL) reconstruction algorithm (or equivalently, maximum a posteriori reconstruction), where the sum of the likelihood and a noise suppressing penalty term (or Bayesian prior) is optimized. Usually, this approach yields position-dependent resolution and bias. However, for some applications in emission tomography, a shift-invariant point spread function would be advantageous. Recently, a new method has been proposed, in which the penalty term is tuned in every pixel to impose a uniform local impulse response. In this paper, an alternative way to tune the penalty term is presented. We performed positron emission tomography and single photon emission computed tomography simulations to compare the performance of the new method to that of the postsmoothed maximum-likelihood (ML) approach, using the impulse response of the former method as the postsmoothing filter for the latter. For this experiment, the noise properties of the PL algorithm were not superior to those of postsmoothed ML reconstruction.