二维光学子波变换中输入图像与子波匹配滤波器大小的研究

在应用计算全息制作子波匹配滤波器实现二维光学子波变换的研究中,输入图像与子波匹配滤波器的大小,对于实验结果有较大的影响,给出了简要的理论分析和实验结果,实验结果与计算机模拟完全一致。     

用计算全息制作改进的Mexican-hat子波匹配滤波器实现二维光学子波变换

提出了应用计算全息制作改进的Ｍｅｘｉｃａｎ－ｈａｔ子波匹配滤波器,在一个光学４ｆ系统中实现了二维子波变换,给出了简要的理论分析和实验结果,实验结果与计算机模拟完全一致。     

M带离散时间子波变换

二带连续时间子波变换可由无限级树形正交镜像滤波器（ＱＭＦ）组产生,类似地,二带离散时间子波变换可表示为有限级树形ＱＭＦ组,该文对二带离散时间子波变换进行了推广,给出了Ｍ带离散时间子波变换,并研究了Ｍ带离散时间子波变换与Ｍ带仿酉滤波器组之间的关系。结果表明,在Ｌ级Ｍ带树形滤波器组中,如果每级滤波器组仿酉滤波器组,则该树形滤波器组所产生的离散时间子波基是正交基。     

用计算全息法实现二维Morlet子波变换

应用计算全息制作子波匹配滤波器实现光学Morlet子波变换，并给出了Morlet子波变换计算机模拟结果和光学实现的实验结果，两者相一致.     

M带离散时间子波变换

二带连续时间子波变换可由无限级树形正交镜像滤波器(QMF)组产生,类似地,二带离散时间子波变换可表示为有限级树形QMF组。该文对二带离散时间子波变换进行了推广,给出了M带离散时间于波变换,并研究了M带离散时间子波变换与M带仿酉滤波器组之间的关系。结果表明,在L级M带树形滤波器组中,如果每级滤波器组是仿酉滤波器组,则该树形滤波器组所产生的离散时间子波基是正交基。     

利用子波变换的图象压缩编码技术

从1988年Malial将子波交换用于信号处理提出多分辨率分析概念，给出了信号或图象分解为不同频率通道的算法和重构算法，开创了子波变换在图象处理中的应用．于波变换在时域和频域同时具有良好的局部化性能，使子波变换成为视频图象压缩编码的主要技术，专家预计在1996年将会高清晰度电视标准的子波变换视频图象压缩技术将推出问世．本文将综述子波变换图象压缩编码技术当前的进展。     

基于DSP的正交离散子波变换系统的设计与实现

子波变换现已成为一种重要的信号处理方法．本文提出一种以ＴＩ公司ＤＳＰ（ＴＭＳ３２０Ｃ３０）为基础的一维正交离散子波变换系统的实现方法，用该系统实现 Ｍａｌｌａｔ 正交子波快速分解与重构算法，并详细介绍了该系统的电路设计原理和相应的软件流程．测试结果表明该系统重构信号的绝对误差小于１０~( －３)。     

插值子波变换的优化设计

Ｄｅｓｌａｕｒｉｅｒｓ－ｄｕｂｕｃ子波是一类被广泛采用的插值子波,其不足之处在于系统冗余较大,一般插值子波是Ｄｅｓｌａｕｒｉｅｒｓ－ｄｕｂｕｃ子波的推广,设计上具有更大发为活性,本文研究了一般插值子波系统的性质、优化设计准则以及有效的优化设计方法。最后,通过对实测信号的压缩说明了本文方法的有效性。     

多维多分辨率分析和图像序列的三维子波变换压缩编码

最近有文献报道图像序列的三维子波变换压缩编码。本文讨论了多维多分辨率分析和三维子带系统完全重构的充分必要条件，我们对8帧图像序列进行三级三维子波变换，然后进行零树量化和熵编码，文中给出了不同压缩比下的信噪比。编码器可以在要求的任意压缩比下停止编码．如同图像的二维子波零树编码。     

基于小波变换与形态学运算的ECG自适应滤波算法

针对ECG信号常用滤波算法存在的缺陷,提出了基于小波变换与形态学运算的自适应滤波新算法。该算法利用形态学滤波器去除基线漂移信号,用小波滤波器去除高频干扰信号,并将这两部分所得到的心电噪声分量作为自适应滤波器的参考输入信号,利用自适应滤波器调整对含噪ECG信号进行滤波处理。最后,经实验验证了本文算法的有效性。     

A fast Discrete Wavelet Transform algorithm for visual processing applications

For visual processing applications, the two-dimensional (2-D) Discrete Wavelet Transform (DWT) can be used to decompose an image into four-subband images. However, when a single band is required for a specific application, the four-band decomposition demands a huge complexity and transpose time. This work presents a fast algorithm, namely 2-D Symmetric Mask-based Discrete Wavelet Transform (SMDWT), to address some critical issues of the 2-D DWT. Unlike the traditional DWT involving dependent decompositions, the SMDWT itself is subband processing independent, which can significantly reduce complexity. Moreover, DWT cannot directly obtain target subbands as mentioned, which leads to an extra wasting in transpose memory, critical path, and operation time. These problems can be fully improved with the proposed SMDWT. Nowadays, many applications employ DWT as the core transformation approach, the problems indicated above have motivated researchers to develop lower complexity schemes for DWT. The proposed SMDWT has been proved as a highly efficient and independent processing to yield target subbands, which can be applied to real-time visual applications, such as moving object detection and tracking, texture segmentation, image/video compression, and any possible DWT-based applications.     

A Survey on Lifting-based Discrete Wavelet Transform Architectures

In this paper, we review recent developments in VLSI architectures and algorithms for efficient implementation of lifting based Discrete Wavelet Transform (DWT). The basic principle behind the lifting based scheme is to decompose the finite impulse response (FIR) filters in wavelet transform into a finite sequence of simple filtering steps. Lifting based DWT implementations have many advantages, and have recently been proposed for the JPEG2000 standard for image compression. Consequently, this has become an area of active research and several architectures have been proposed in recent years. In this paper, we provide a survey of these architectures for both 1-dimensional and 2-dimensional DWT. The architectures are representative of many design styles and range from highly parallel architectures to DSP-based architectures to folded architectures. We provide a systematic derivation of these architectures along with an analysis of their hardware and timing complexities. Tinku Acharya received his B.Sc. (Honors) in Physics, B.Tech. and M.Tech. in Computer Science from University of Calcutta, India, and the Ph.D. in Computer Science from University of Central Florida, USA, in 1984, 1987, 1989, and 1994, respectively. He is currently the Chief Technology Officer of Avisere Inc., Tucson, Arizona, USA. Dr. Acharya is also an Adjunct Professor in the Department of Electrical Engineering, Arizona State University, Tempe, USA. Before joining Avisere, Dr. Acharya served in Intel Corporation (1996–2002), where he led several R&D teams toward development of algorithms and architectures in image and video processing, multimedia computing, PC-based digital camera, reprographics architecture for color photo-copiers, 3G cellular telephony, analysis of next-generation microprocessor architecture, etc. Before Intel, Dr. Acharya was a consulting engineer at AT&T Bell Laboratories (1995–1996), a research faculty at the Institute of Systems Research, Institute of Advanced Computer Studies, University of Maryland at College Park (1994–1995), and held visiting faculty positions at Indian Institute of Technology, Kharagpur. He served as Systems Analyst in National Informatics Center, Planning Commission, Government of India (1988–1990). He collaborated in research and development with Xerox Palo Alto Research Center (PARC), Eastman Kodak Corporation, and many other institutions worldwide. Dr. Acharya is inventor of 88 US patents and 14 European patents. He authored over 80 technical papers and four books—Image Processing: Principles and Applications (Wiley, New Jersey, 2005), JPEG2000 Standard for Image Compression: Concepts, Algorithms, and VLSI Architectures (Wiley, 2004), Information Technology: Principles and Applications (Prentice-Hall India, 2004), and Data Mining: Multimedia, Soft Computing and Bioinformatics (Wiley, 2003). Dr. Acharya is a Fellow of the National Academy of Engineers (India), Life Fellow of the Institution of Electronics and Telecommunication Engineers (FIETE), and Senior Member of IEEE. His current research interests are in computer vision, image processing, multimedia data mining, bioinformatics, and VLSI architectures and algorithms. Chaitali Chakrabarti received the B.Tech. degree in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, India in 1984, and the M.S. and Ph.D degrees in electrical engineering from the University of Maryland at College Park, USA, in 1986 and 1990 respectively. Since August 1990, she has been with the Department of Electrical Engineering, Arizona State University, Tempe, where she is now a Professor. Her research interests are in the areas of low power embedded systems design including memory optimization, high level synthesis and compilation, and VLSI architectures and algorithms for signal processing, image processing and communications. Dr. Chakrabarti is a member of the Center for Low Power Electronics, the Consortium for Embedded Systems and Connection One. She received the Research Initiation Award from the National Science Foundation in 1993, a Best Teacher Award from the College of Engineering and Applied Sciences, ASU, in 1994, and the Outstanding Educator Award from the IEEE Phoenix section in 2001. She has served on the program committees of ICASSP, ISCAS, SIPS, ISLPED and DAC. She is currently an Associate Editor of the IEEE Transactions on Signal Processing and the Journal of VLSI Signal Processing Systems. She is also the TC Chair of the sub-committee on Design and Implementation of Signal Processing Systems, IEEE Signal Processing Society.     

小波变换码中滤波器的选择

在本文中，我们从广义编码增益、分解重构性能与对误差的敏感性、编码性能与主观质量三个方面对双正交小波12／4、9／7与9／3进行比较，得出：双正交小波12／4比9／7与9／3更适合于小波图像压缩，特别是在高保真压缩中；在低比特率编码中，双正交小波12／4与9／7相差不大。     

基于System Generator的二维离散小波变换的算法实现

结合传统的Mallat算法,利用System Generator平台,提出了一种高速二维小波变换的方法。通过实例图像(64×64)在该平台中开发相关的模块,并进行仿真和逻辑综合,最后充分体现了在System Generator中实现小波变换的优越性。     

基于子波变换阈值决策的非稳信号去噪

本文通过对Donoho阈值决策子波域去噪方法进行研究,该方法采用软限幅函数对噪声信号的子波变换系数做阈值处理以达到去噪的目的。接着具体讨论了在Sym8子波基底下,用此方法对非稳雷达回波进行五尺度的Mallat算法仿真,结果表明该方法抑制噪声效果良好且简单易行,是传统匹配滤波无法比拟的。     

基于信噪比门限判断和小波变换的SAR干涉图滤波法

该文首先研究了一种基于离散小波变换(DWT)的干涉图滤波算法,对该算法的噪声模型和处理流程进行了详细的分析,并在其基础上做了基于静态小波变换(SWT)的改进。接着利用实测数据对这两种方法做了实验,通过对实验结果的分析,提出了一种高噪声环境下,在保证残点数降低率的同时,还能提高干涉条纹质量的滤波方法。在此滤波方法的基础上, 进一步提出了基于信噪比门限判断的干涉图两级处理滤波法,并对其处理流程做了详细的讨论。利用实测数据对该方法进行了仿真,实验结果验证了该方法的有效性。     

基于DSP Builder的二维提升小波变换实现

离散小波变换在图像压缩处理中有着重要的作用,并得到了广泛的应用.与传统的基于卷积的架构相比较,基于提升的架构具有需要较少的硬件资源,占用较少的芯片面积等优点.在DSP Builder中实现了基于提升的一维离散小波变换,并通过构造相关的存储器控制逻辑,完成了二维离散小波变换架构的设计.利用该架构对图像进行离散小波变换,与软件变换的结果相比较,并计算出图像的峰值信噪比,验证了其正确性.     

基于整数小波变换的有损与无损嵌入式图像压缩

提出了基于整数小波变换的嵌入式图像编码方案，能够进行有损和无损两种模式的图像压缩，在非常低的比特率时能够获得较高质量的重构图像。该方法适合图像的渐进传输，它是使用扩增的零树信号和位面编码的合成技术，最后对产生的符号流进行算术编码。文中给出了该方法与有损和无损压缩标准的性能比较。