基于改进的K-L变换的多光谱图像压缩算法

融合离散小波变换(DWT,discrete wavelet tran sform)与Karhunen-Loeve变换(KLT),将图像的能量集中到少数系数上,以达到更好的 压缩效果。首先将多光谱图像的每个谱段进行快速9/72D DWT,消除多光谱图像的大部分 空间冗余;然后对所有谱段产生的小波系数进行改进的KLT,来消除光谱冗余和残存的空 间冗余;最后对所得谱段产生的小波系数进行改进的KLT,来消除光谱冗余和残存的空间冗 余;最后对所得系数进行熵编码,得到压缩码流。实验结果表明,在码率为0.25～2.0bit/ pixel范围内,平均信噪比(SNR)高于41dB,同时缩短了运 算时间,从而提升了多光谱图像压 缩算法的性能。     

基于整数小波变换的实时图像压缩编码算法

随着数字图像压缩编码技术的广泛应用,人们对于压缩编码技术的实时性要求越来越高,如何进行快速有效的图像压缩编码是当前需要解决的关键技术。文中在分析提升算法和基于提升算法的整数小波变换的基础上,介绍了一种新的基于整数小波变换和树特征平面的数字图像编码方法,该算法只需对小波系数进行一次扫描即可完成编码过程,简单易行。实验证明该算法性能优良,并且与零树编码相比大大节约了处理时间。     

基于重要图中止符的变树结构的零树嵌入式编码算法

本文提出了一种易于硬件实现的高速优质的零树嵌入式编码算法.通过精简重要图结构,消除结构中的一些冗余、减小一些无用的零树、消除一些不必保留的孤立零点,来提高编重要图的效率,同时也提高了压缩效果、加快了编解码速度.采用提升方案构造双正交9/7小波,不仅进一步提高了小波变换速度,而且为硬件实现提供了保障.经模拟实验表明:此方法加快小波变换速度,缩短了编、解码时间,提高了图像压缩比和恢复图像质量.     

三种数字图像压缩算法性能的研究

基于数字图像压缩的可能性与必要性,本文用信息论观点讨论了数字图像压缩的方法。并分别采用差分脉冲编码调制、离散余弦变换、离散小波变换等3种方法,对Lena(512×512)图片进行处理,统计处理后的系数的概率分布、标准差、信息熵,以及增大量化阈值后0系数所占的比率,来研究他们用于数字图像压缩的性能。同时,还给出了每种方法的压缩重建的误差分析。并做了详细地比较,分析了导致他们性能差异的原因,这对于进一步学习和寻找更好的数字图像压缩方法具有一定的指导意义。     

浅析城市电视台非线性网络编辑及硬盘播出系统

进入90年代以来，数字图像信号的压缩技术获得了长足进步。在电视台的制作和播出系统中开始向全面的数字化转化。经过数字化以后的电视信号最适合于利用计算机进行处理；另一方面，计算机多媒体技术发展的趋势非常迅猛，功能不断增强，为电视信号的处理提供了条件，电视台的制作和播出系统开始发生根本性的变化。1 非线性编辑的核心技术非线性编辑的核心技术，就是视频编码压缩技术，主要采用6种不同的压缩方法：小波变换（Wavelet），M-JPEG，MPEG-2，DV和ITU-R601，DVCPRO。（1）小波变换压缩小波变换是根据图像统计特性和人眼的…     

一种基于小波的多光谱图像压缩方法

文章提出一种基于Karhunen2Loeve变换(KLT)和小波量化编码的多光谱图像压缩方法。该法首先使用KL变换步骤来去除谱间冗余,而后对各变换波段图像进行小波变换,并利用均匀阈值网格编码量化方法来量化小波子带图像,最后使用算术编码对量化结果进行熵编码。为使编码器能为所有谱段各子带获取率- 失真意义上最优的量化阈值,本文提出基于子带图像统计特性和网格编码量化器率- 失真特性的比特分配算法。实验表明,本方法能高效地压缩多光谱图像,表现出优异的压缩性能。     

一种新的二维自适应提升小波变换方法

基于图像的局部特性和统计信息,该文提出一种新的二维自适应提升小波变换方法。该方法通过构造基于图像局部特性的自适应更新算子和基于图像统计信息的非线性预测算子,对图像进行自适应的提升小波变换,与文献中的自适应小波变换方法和非自适应小波变换方法进行对比实验,该文方法所得到的高频子带的熵更低,含零高频系数更多,更有利于图像的压缩编码。     

基于离散余弦变换的图像压缩研究

图像压缩是数据压缩技术在数字图像上的应用,其目的是减少图像数据中的冗余信息,从而用更加高效的格式存储和传输数据。图像压缩可以是有损数据压缩也可以是无损数据压缩。这里所研究的是有损压缩技术,对于此类技术,变换编码是最常用的方法,如离散余弦变换（DCT）或者小波变换这样的傅里叶相关变换,然后进行量化和用熵编码法压缩。     

整数小波域张量Tucker分解的多光谱图像压缩

针对常用多光谱图像压缩算法-把图像作为矩阵向量量化后再进行相应压缩处理-导致图像的本征结构被破坏、信息损失以及压缩效率低等问题,提出一种基于非负张量(NTD)Tucker分解的多光谱图像压缩算法。首先将多光谱图像的每个谱段进行二维提升整数5/3小波变换,消除多光谱图像的空间冗余。然后将所有谱段的每级小波变换的4个小波子带看作为4个NTD。对每个非负小波子带张量采用改进局部HALS-NTD算法进行Tucker分解,消除光谱冗余和空间残余冗余。最后,将分解的核心张量和模式矩阵进行熵编码。实验结果表明,本文提出的压缩算法具有良好压缩性能,在压缩比32∶1～4∶1范围内,平均信噪比(SNR)高于40dB,与传统多光谱图像压缩算法比较,提高了1.779dB,有效提高了多光谱图像压缩算法的压缩性能。     

基于帧间去相关的超光谱图像压缩方法

针对超光谱图像的特点和硬件实现的实际需要,提出了一种基于小波变换的前向预测帧间去相关超光谱图像压缩算法。通过图像匹配和帧间去相关,消除超光谱图像帧间的冗余,对残差图像的压缩采用基于小波变换的快速位平面结合自适应算术编码的压缩算法,按照率失真准则控制输出码流,实现了对超光谱图像的高保真压缩。通过实验证明了该方案的有效性,基于小波变换的快速位平面结合自适应算术编码的压缩算法速度优于SPIHT,而且易于硬件实现。     

A new framework for complex wavelet transforms

Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce two-stage mapping-based complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, non-redundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and non-redundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we can exploit this flexibility to create the complex double-density DWT (CDDWT): a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3/sup m/-1/2/sup m/-1) in m dimensions. To the best of our knowledge, no other transform achieves all these properties at a lower redundancy.     

Multidimensional, mapping-based complex wavelet transforms.

Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3M - 1)/(2M - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processing applications, we have demonstrated state-of-the-art results.     

Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms

In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. We use the denoising capabilities of decimated and undecimated multiwavelet transforms, DMWT and UMWT respectively, for the removal of noise from microarray data. Multiwavelet transforms, with appropriate initialization, provide sparser representation of signals than wavelet transforms so that their difference from noise can be clearly identified. Also, the redundancy of the UMWT transform is particularly useful in image denoising in order to capture the salient features such as noise or transients. We compare this method with the discrete and stationary wavelet transforms, denoted by DWT and SWT, respectively, and the Wiener filter for denoising microarray images. Results show enhanced image quality using the proposed approach, especially in the undecimated case in which the results are comparable and often outperform that of the stationary wavelet transform. Both multiwavelet transforms outperform the DWT and the Wiener filter.     

离散小波变换域非负张量分解的高光谱遥感图像压缩

该文提出一种基于非负张量分解的高光谱图像压缩算法.首先将高光谱图像的每个谱段进行2维离散5/3小波变换,消除高光谱图像的空间冗余.然后将所有谱段的每级小波变换的4个小波子带看作为4个张量.对每个小波子带张量采用改进HALS(Hierarchical Alternating Least Squares)算法进行非负分解,来消除光谱冗余和空间残余冗余,同时保护了光谱信息.最后,将分解的因子矩阵进行熵编码.实验结果表明,该文提出的压缩算法具有良好压缩性能,在压缩比32:1～4:1范围内,平均信噪比高于40 dB,与传统高光谱图像压缩算法比较,平均峰值信噪比提高了1.499 dB.有效地提高了高光谱图像压缩算法的压缩性能和保护了光谱信息.     

一种用于版权保护的音频数字水印算法

研究了一种基于小波变换的音频数字水印算法，水印为一幅二值图像，仿真实验表明该算法具有较强的稳健性，可用于数字音产品的版权保护。     

Defect detection of IC wafer based on two-dimension wavelet transform

Defect detection of integrated circuit (IC) wafer based on two-dimension wavelet transform (2-D DWT) is presented in this paper. By utilizing the characteristics many of the same chips in a wafer, three images with defects located in the same position and different chips are obtained. The defect images contain the standard image without any defects. 2-D DWT presented in the paper can extract the standard image from the three defect images. The algorithm complexity of the method is close to that of 2-D DWT. After obtaining the standard image, the speed and accuracy of defects detection can be greatly enhanced using the detection method presented in the paper. Using the image gray-scale matching technology, impact of illumination on IC defect detection is solved. Experiments demonstrate that 2-D DWT is fast and accurate to defects detection in an IC image, and the method has high robustness for illumination.     

CCSDS中二维整数小波变换的FPGA实现方法

CCSDS空间图像压缩标准(CCSDS 122.0-B-1)的核心算法之一是三级二维小波变换,此变换适合用可编程逻辑电路实现。文章介绍了整数9/7小波变换的特点,提出了一种基于FPGA的二维变换快速实现结构,该方法利用FPGA内部Block RAM进行行暂存,实现了行列同时变换的效果,节省了内部寄存器资源,并获得了较高的数据吞吐率。在此基础上,文章还给出了两种适用于不同需求的多级变换架构,并通过仿真验证了其合理性。     

基于正交小波变换的图像去噪算法研究

提出了一种基于正交小波变换的图像去噪方法,首先利用离散小波对图像信号按Mallat算法进行分解,然后采用软闽值与小波重构的算法进行去噪。深入研究了小波变换中的图像分解与重构的Mallat算法,详细介绍了正交小波变换中阈值的选取,并进行了实验研究。实验结果表明,该方法可以有效去除噪声,并保留了图像细节部分的有用信息。     

一种混合噪声图像的自适应滤除方法

基于小波变换的阈值去噪方法仅适用于去除高斯白噪声,对于脉冲噪声得不到好的去噪效果,正交小波变换由于缺乏平移不变性,在去噪过程中会产生人为的振荡现象,使图像边缘失真,甚至图像模糊,提出了基于平稳小波域自适应阈值算法同中值滤波相结合的去噪方法,该方法能有效地滤除图像中的高斯白噪声和脉冲噪声组成的混合噪声,并验证了该方法的有效性。