Phase retrieval in digital speckle pattern interferometry by use of a smoothed space-frequency distribution

We evaluate the use of a smoothed space-frequency distribution (SSFD) to retrieve optical phase maps in digital speckle pattern interferometry (DSPI). The performance of this method is tested by use of computer-simulated DSPI fringes. Phase gradients are found along a pixel path from a single DSPI image, and the phase map is finally determined by integration. This technique does not need the application of a phase unwrapping algorithm or the introduction of carrier fringes in the interferometer. It is shown that a Wigner-Ville distribution with a smoothing Gaussian kernel gives more-accurate results than methods based on the continuous wavelet transform. We also discuss the influence of filtering on smoothing of the DSPI fringes and some additional limitations that emerge when this technique is applied. The performance of the SSFD method for processing experimental data is then illustrated.     

彩色图像量化的FSCAMMD聚类算法

提出了一种基于模式识别技术的彩色图像量化的新算法－－基于最小距离最大的快速统计聚类算法（FSCAMMD）。本算法克服了SCA算法对聚类中心初始值选取的不足,给出了最大频度与类内最小距离最大相结合的方法－－初始值优选法,实验结果表明,本算法可较大幅度地减少图像量化后的总方差以颜色失真度。     

基于模糊非线性回归算法的图像复原

为了利用多幅具有相似降质特性的退化图像信息恢复出原图像,提出了一种新的基于模糊参量非线性回归估计的图像盲复原算法。 该算法充分利用多幅图像具有的相似降质特征,首先给出一种非理想光照分布参数的模糊非线性回归估计方法,然后通过估计出的参数得到与原图同样大小的非理想光照分布图,最后通过观测图像与非理想光照分布图相消的方法,对退化图像进行复原。 实验结果表明,该算法运算快速,对边缘细节及平滑区域均有良好的修复能力,在修复效果上明显优于一般图像修复算法。     

基于假设理论的序列图像中多目标检测和跟踪(英文)

本文提出一种基于目标检测的多维假设多目标检测和跟踪方法,此算法对于序列图像的照明变化和遮蔽现象具有很高的鲁棒性.首先,对序列图像进行背景抑制、时域滤波和杂波剔除的预处理,得到单帧的初始目标检测结果.对于初始检测中存在的漏检和误检现象,采用基于假设理论的跟踪概率模型优化初始检测轨迹;将目标的跟踪信息反馈于目标检测模块,形成一闭环自适应跟踪系统,达到多目标的最优检测和跟踪.实验结果表明了所提出的方法在多目标检测和跟踪中的可行性和有效性.     

Image restoration using a two-dimensional lorentzian probability model

Abstract

It is known that the distribution of intensity gradients along separate horizontal and vertical directions in an image of a general scene often has a sharp peak with a long tail. This property, can be described by a Lorentzian probability function, and is the basis of an efficient nonlinear one-dimensional restoration algorithm. It can also superresolve a two-dimensional separable image. This paper discusses the gradient distribution in a general two-dimensional image and shows that the distribution of the maximum gradient at any picture point is also Lorentzian. This has been used to develop an iterative two-dimensional restoration algorithm. It starts by evaluating the likelihood of the intensity gradients within a Wiener filtered image. Then a nonlinear correction term is introduced which increases this likelihood under mean square error criterion. The method is applied to synthetic images and to a 94 GHz passive millimetre wave image. This new two-dimensional method is shown to be superior to the previous one-dimensional algorithm which had to be applied separately along two orthogonal directions.     

基于图像纹理特征的目标快速检索

在讨论共生矩阵的基础上,提出了一个通过图像分割获取目标图像纹理特征,进而实现图像快速检索的方法。试验表明,该方法检索目标图像的可靠性较高,具有良好的应用价值。     

Simulation of an active underwater imaging through a wavy sea surface

A numerical simulation for underwater imaging through a wavy sea surface has been done. We have used a common approach to model the sea surface elevation and its slopes as an important source of image disturbance. The simulation algorithm is based on a combination of ray tracing and optical propagation, which has taken to different approaches for downwelling and upwelling beams. The nature of randomly focusing and defocusing property of surface waves causes a fluctuated irradiance distribution as an illuminating source of immersed object, while it gives rise to a great disturbance on the image through a coordinate change of image pixels. We have also used a modulation transfer function based on Well’s small angle approximations to consider the underwater optical properties effect on the transferring of the image. As expected, the absorption effect reduces the light intensity and scattering decreases image contrast by blurring the image.     

一种基于动态误差扩散系数的数字半色调算法

目的为了解决现有半色调算法产生的图像边界模糊以及视觉蠕虫等问题,研究一种视觉效果较好的半色调图像。方法提出一种在半色调过程中动态分配误差扩散系数的数字半色调方法,减少由固定误差扩散系数和固定扩散方向带来的边界模糊、视觉蠕虫以及结构性纹理等现象。结果文中提出的算法相较于传统的误差扩散算法其PSNR值提高了约0.5,SSIM值提高了约0.06,NSME值下降了约0.06。不仅解决了传统误差扩散算法中的边界模糊现象,同时也更好地表达了半色调图像的细节纹理信息。结论依据提出的算法产生的半色调图像视觉效果较好,并且算法简单易行,运行效率高。     

结合Zernike矩的多尺度模板形状匹配

针对形状匹配中小波表达对起始点依赖的问题,提出一种结合Zernike矩的多尺度模板进行形状匹配的方法。该算法对输入图像进行预处理后提取目标轮廓,经过归一化处理得到目标形状的平移、尺度不变的链状表达,再通过小波变换进行多尺度分析;引入Zernike矩,利用Zernike矩的特性,实现小波表达的旋转不变性,解决了小波变换对起始点的依赖。匹配过程是以小波表达的各阶Zernike矩为特征向量,在由粗到精的尺度上进行的。实验结果表明,对于同一目标,原图像与旋转不同角度的图像的正确匹配率为91％。该算法适用于轮廓较明显的目标。     

Image deconvolution using incomplete Fourier measurements

In this article, we propose a new deconvolution algorithm, which is based on image reconstruction from incomplete measurements in Fourier domain. Our algorithm has two steps. First, an initial estimator is obtained using Fourier regularized inverse operator. Second, parts of the estimator's Fourier coefficients are saved, and the others are removed to suppress noise energy, then the remaining coefficients are used to recover image based on the sparse constraints. This image reconstruction problem is an optimization problem that is solved by a fast algorithm named split Bregman iteration. Different from other deconvolution algorithms, our algorithm only uses parts of Fourier components to restore the blurred image and combines two different regularization strategies efficiently by applying a selection matrix. The experiment shows that our method gives better performance than many other competitive deconvolution methods. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 233–240, 2012     

Image deblurring based on ForIcM: Fourier shrinkage and incomplete measurements

Abstract

In this paper, we propose a new deblurring algorithm, which is based on image reconstruction from incomplete measurements in Fourier domain. Our algorithm has two steps. Firstly, an initial estimator is obtained using Fourier regularised inverse operator. Secondly, parts of the estimator’s Fourier coefficients are saved, and the others are removed to suppress noise energy, and then the remaining coefficients are used to recover image based on the sparse constraints. This image reconstruction problem is an optimisation problem which is solved by a fast algorithm named split Bregman iteration. Our algorithm combines two different regularisation strategies efficiently by applying a selection matrix. The tests using images with different blurs and noise produce good results. The experiment shows that our method gives better performance than many other competitive deblurring methods.     

基于HSV空间的岩心图像处理

针对石油勘探中的岩心图像在分割过程中难以有效识别出灰度值相近的颗粒与颗粒、颗粒与背景的边缘信息,且容易造成过度分割或欠分割的问题,提出一种彩色图像分割并获取颗粒粒度分布的算法,并将结果与灰度迭代算法、最大熵法和分水岭算法分割结果进行比较。结果表明,该算法以彩色图像HSV模型的V分量为依据进行图像分割,可以在较为复杂的岩心背景下校正分割结果,更准确地分割出目标和背景颜色相近的边缘,获得砾石、泥沙颗粒的粒度分布。     

Image reconstruction algorithm based on the extended regularised total least squares method for electrical capacitance tomography

Electrical capacitance tomography (ECT) is a non-invasive imaging technology that aims at the visualisation of the cross-sectional permittivity distribution of a dielectric object based on the measured capacitance data. Successful applications of ECT depend greatly on the precision and speed of the image reconstruction algorithms. ECT image reconstruction is a typical ill-posed problem, and its solution is unstable, that is, the solution is sensitive to noises in the input data. Methods that ensure the stability of a solution while enhancing the quality of the reconstructed images should be used to obtain a meaningful reconstruction result. An image reconstruction algorithm based on the regularised total least squares (TLS) method that considers the errors in both the sensitivity field matrix and the capacitance data for ECT is presented. The regularised TLS method is extended using a combination robust estimation technique and an extended stabilising functional according to the ill-posed characteristics of ECT, which transforms the image reconstruction problem into an optimisation problem. In addition, the Newton algorithm is employed to solve the objective functional. Numerical simulations indicate that the algorithm is feasible and overcomes the numerical instability of ECT image reconstruction; for the cases of the reconstructed objects considered here, the spatial resolution of the reconstructed images obtained using the algorithm is enhanced; as a result, an efficient method for ECT image reconstruction is introduced.     

纹理贴图模型的彩色截面轮廓生成算法研究

针对彩色3D打印彩色轮廓生成问题,本文提出一种基于纹理贴图模型的彩色轮廓生成算法。根据平面投影法建立纹理和三维模型的映射关系,接着对该模型进行切片处理取得轮廓顶点的几何信息和纹理信息;并根据轮廓顶点和纹理图片的映射关系,插值出顶点间色彩信息;同时给出一种内外轮廓的快速识别算法,并提出一种基于偏置算法的彩色轮廓偏置方法,形成颜色厚度,消除阶梯效应。     

Improved inversion procedure for particle size distribution determination by photon correlation spectroscopy

We propose a minimum variation of solution method to determine the optimal regularization parameter for singular value decomposition for obtaining the initial distribution for a Chahine iterative algorithm used to determine the particle size distribution from photon correlation spectroscopy data. We impose a nonnegativity constraint to make the initial distribution more realistic. The minimum variation of solution is a single constraint method and we show that a better regularization parameter may be obtained by increasing the discrimination between adjacent values. We developed the S-R curve method as a means of determining the modest iterative solution from the Chahine algorithm. The S-R curve method requires a smoothing operator. We have used simulated data to verify our new method and applied it to real data. Both simulated and experimental data show that the method works well and that the first derivative smoothing operator in the S-R curve gives the best results.     

一种高效的图像去噪算法

针对图像去噪的速度以及可能出现的阶梯效应等问题进行了研究,提出了一种高效的图像去噪算法.该算法在贝叶斯框架下,首先引入调和模型作为原始图像的先验模型,并用伽马分布作为未知参数的先验分布模型;然后,用变分近似的方法推导最大后验概率;基于此推导过程,同步地估计原始图像和未知参数的最优值,实现图像去噪.实验结果证明了该算法的高效性,通过与其它算法的比较,该算法体现了速度快、效果好的优点,且去噪后的图像不会出现阶梯效应等问题.     

Image Restoration Using a Lorentzian Probability Model

Abstract

The distribution of edge values for an image of a general scene often has a sharp peak with a long tail. This property, which can be well described by a Lorentzian probability function, has been used to develop an efficient nonlinear image restoration algorithm for reducing the various artifacts that often arise in the restored images. The algorithm starts with a Wiener filter solution which is used to model the edge image by the Lorentzian function so that the likelihood of the image can be estimated. A nonlinear correction term is then introduced which increases this image likelihood under the mean square error criterion. This process ensures that the resulting image retains its sharpness while reducing the noise and ringing artifacts. An iterative procedure has been developed to implement this method. Computer simulated results show that the algorithm is robust in reducing artifacts and easily implemented. The algorithm also possesses a superresolution capability due to the highly nonlinear property of the correction term.     

Cellular automaton algorithm for surface mass transport due to curvature gradients simulations of sintering

A cellular automaton algorithm is developed that simulates the evolution of a surface due to surface mass transport. The driving force is the reduction of chemical potential differences on the surface. This process is important in the development of microstructure during the sintering of powders. The algorithm is implemented in 2D in a digital image mode, using discrete pixels to represent continuum objects. The heart of the algorithm is a pixel-counting-based method for computing the potential at a pixel located in a digital surface. This method gives an approximate measure of the curvature at the given surface pixel. The sontinuum version of this method is analytically shown to give the true curvature at a point on a continuum surface. The digital version of the curvature computation method is shown to obey the scaling laws derived for the continuum version. Several examples, both quantitative and qualitative, are computed of surfaces evolving under curvature differences, and are shown to agree with the known physics of sintering.     

一种视频序列的背景提取算法

针对传统背景提取算法的不足,提出一种新的视频序列背景提取方法。根据高斯分布建模图像每个像素位置背景,判断观测值是否和背景匹配,据此决定采用递推更新背景或是批处理更新背景。若高斯分布模型判断观测值和背景模型匹配,则更新高斯模型参数,并使用最小二乘法递推更新背景;对连续判断不匹配背景的像素则采取批处理方法更新背景。实验表明,和其它背景提取算法相比,该算法提取的背景效果好,能有效去除噪声和消除前景的运动模糊。     

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.