Probabilistic linear models for multiresolution estimation in gray-level images

A multiresolution analysis of digital gray-level images is presented. A gray-level multi-scale framework is determined from two main assumptions: the gray scale is binary at the finest spatial resolution, and the gray levels of composed regions are obtained additively. In order to interrelate the gray-level histograms of the same image at different resolutions, probabilistic linear models are developed, which are then applied for estimation. Linear-optimization theory is used as a way of constructing such models. A general procedure for image processing is sketched, based on gray-level estimation. A versatile algorithm for nonlinear filtering is derived. Some examples of prospective applications are given.This work was partially supported by grant TIC91-646 from the DGYCIT of the Spanish Government.     

Multiresolution coding techniques for digital television: A review

Multiresolution decompositions for video coding are reviewed. Both nonrecursive and recursive coding schemes are considered. In nonrecursive schemes, it is shown that pyramid structures have certain advantages over subband or wavelet techniques, and a specific spatiotemporal pyramid coding of HDTV is discussed in some detail. It is shown that recursive, DPCM like schemes will incur a slight loss of optimality due to a restricted form of prediction if multiresolution decomposition with compatible decoding is required. Compatibility and transmission issues are also discussed. Multiresolution transmission for digital broadcast TV is introduced. This, when combined with multiresolution source coding, achieves spectrum efficiency, robustness and graceful degradation under channel impairments.Invited PaperWork supported in part by the National Science Foundation under grants ECD-88-11111, MIP-90-14189 and Bell Communications Research.Work supported by the National Science Foundation under grants ECD-88-11111. K.M. Uz is now with David Sarnoff Research Center in Princeton, NJ 08543.     

Terrestrial digital video broadcasting for mobile reception using OFDM

Performance of a system design for digital video broadcasting is examined with emphasis on mobile reception. Orthogonal frequency division multiplexing (OFDM) is used to achieve good bandwidth efficiency and to mitigate the intersymbol interference resulting from the channel delay spread. The resulting equivalent channel including OFDM can be modeled as a flat Rayleigh fading channel plus an interchannel interference (ICI) term due to the channel Doppler spread. This ICI term is analyzed and shown to result in an error floor. Performance improvements due to antenna diversity and trellis-coded modulation (TCM) are given. Finally, multiresolution modulation is discussed as a means of achieving graceful degradation and giving degrees of freedom for further performance improvement.This research was supported by the Multimedia Systems R & D Laboratory, Hitachi, Ltd.     

超声波压力检测中回波搜索算法

通过对超声波压力测量中回波信号特征的分析，提出了回波自动搜索算法。该方法把快速傅立叶变换方法和小波多分辨率信号分解相结合，精确地检测出回波的包络信号，从而准确地搜索到回波，为后续的压力信号处理及提高压力测量的精度奠定了基础。     

小波域声呐图像自适应增强

声呐图像受噪声污染严重、对比度低,给后期的定位识别带来不便,而传统的处理方法容易造成边缘模糊.针对这一问题,提出了一种图像自适应增强算法.该算法利用形态小波对声呐图像进行自适应的多分辨率分析,分别增强不同尺度上的信号或细节,通过多通道重构图像的加权实现去噪和对比度提高.仿真结果表明该算法快速有效,对高斯噪声和冲击性噪声都具有较好的鲁棒性,处理后的声呐图像边缘细节信息保留完好,得到了理想的增强效果.     

采用多分辨率广义S变换的电能质量扰动识别

为提高电能质量复合扰动识别能力,提出一种采用多分辨率广义S变换(multiresolution generalized S-transform,GST)的扰动识别方法.首先,将信号频谱分为低频、中频、高频3个频域,分别设定窗宽调整因子,使其在各个频域具有不同的时-频分辨率,满足不同扰动信号识别要求.并针对高频振荡识别问题,设计基于基频傅里叶谱特征的自适应窗宽调整方法.在此基础上,提取6种特征用于构建决策树.最后,提出最小分类损失原则,确定决策树节点分类阈值,设计扰动分类器.仿真与实测信号实验证明,新方法能够准确识别含5种复合扰动在内的13种扰动.相较于S变换、广义S变换和Hyperbolic S变换,新方法具有更好的特征表现能力,分类效果好,抗噪声干扰能力强.     

带 2细分结构的四边形化

在Morse函数理论的基础上,提出一种新的从三角网格中建立四边形网格多分辨率表示的方法．先由人工指定或从拉普拉斯矩阵的特征函数中提取临界点,计算带约束的拉普拉斯方程得到光滑的Morse函数．函数的临界点（极大、极小和鞍点）有规律地分布在模型表面,在三角网格表面梯度场的引导下, 生成临界点间流线,得到临界点间的拓扑关系．通过临界点交换规则,同样是采用流线的方法,得到更精细的四边形网格。 最终可实现无需参数化而仅用流线方法来建立不同多分辨率表示的四边形网格。     

A Vector-based Representation for Image Warping

A method for image analysis, representation and re-synthesis is introduced. Unlike other schemes it is not pixel based but rather represents a picture as vector data, from which an altered version of the original image can be rendered. Representing an image as vector data allows performing operations such as zooming, retouching or colourising, avoiding common problems associated with pixel image manipulation. This paper brings together methods from the areas of computer vision, image compositing and image based rendering to prove that this type of image representation is a step towards accurate and efficient image manipulation.     

Simultaneous speckle reduction and contrast enhancement for ultrasound images: Wavelet versus Laplacian pyramid

Speckle can be described as random multiplicative noise. It hampers the perception and extraction of fine details in the image. Speckle reduction techniques are applied to ultrasound images in order to reduce the noise level and improve the visual quality for better diagnoses. It is also used as preliminary treatment before segmentation and classification. Several methods have been proposed for speckle reduction in ultrasound images. Multiscale contrast enhancement has proven to be very efficient for x-ray images. A recent study by Dippel et al. doing a comparison, contrast enhancement of radiographs (x-ray and mammography), between the Laplacian pyramid and the wavelet one proves that the Laplacian pyramid method gives a better result than the wavelet one; the filtering aspect was not taken into account. In ultrasound images a strong contrast variation exists which is different from x-ray and mammography. In this paper a wavelet pyramid with simultaneous speckle reduction and contrast enhancement was applied for the first time on ultrasound images with the area of interest and compared to a Laplacian enhancement pyramid. The optimum choice of wavelet bases for ultrasound images is investigated in this study. In order to realize a fair comparison, the same nonlinear modification in both multiscale schemes is used. The comparison proves that the wavelet pyramid gives a much better result than the Laplacian one for simultaneous speckle reduction and contrast enhancement of ultrasound images. The text was submitted by the author in English. Ali Samir Saad, 1964. 1996 PhD in image processing, Polytechnics School of the Engineering University of Nantes, France. 1993 Masters in Electronics. 1990 Masters in Digital Image Processing, Institute of Computer Sciences and Communication University of Rennes, France. 1989 BS in Electrical Engineering, University of Saint-Etienne. Academy of Lyon, France. 1996–2000 Research associate at the National Center for Macromolecular Imaging. Baylor, Houston, Texas. Assistant professor at King Saud University, Dept. of Biomedical Technology. Area of research in medical image processing and analysis, 23 publications, member of the American Association for the Advancement of Sciences. Marquis Who’s Who in the World; Cambridge Blue Book 2006.     

基于三维分枝模型的树木模拟

虚拟自然场景的实时生成一直是图形学研究领域中一个富有挑战性的难题,作为自然场景的重要组成部分,树木的模拟也得到了广泛的重视.本文在实际建模中以三维分枝模型为基础,与随机繁衍L系统相结合,对树木模型进行了一些改进.将光源和重力的影响加入枝段的模型中,使生成树木的形态更加逼真;改进了分枝模式,使得生成树木的随机性得到加强;在绘制时根据不同距离选择不同复杂度的树木模型,加快了渲染的速度.