Wavelet Packet Compression of Medical Images

Tolba, A. S., Wavelet Packet Compression of Medical Images, Digital Signal Processing12 (2002) 441–470The increasing need for efficient image storage and transmission in hospitals imposes heavy requirements on the design of picture archiving and communication systems. Thus new methods are needed for efficient image compression. Recent reviews of wavelets in biomedical applications showed that wavelets should be used with caution and that a particular solution should always be motivated by the problem itself. This study discovers the best design parameters for a data compression scheme applied to medical images of different imaging modalities. The proposed technique aims at reducing the transmission cost while preserving the diagnostic integrity. By selecting the wavelet packet's filters, decomposition level and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimum distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of medical images. Statistical significance tests were performed on the experimental measures to conduct the most suitable wavelet shape for each imaging modality. Image quality measures are used to evaluate the performance of different wavelet filters for different imaging modalities. Image resolution is found to have a remarkable effect on the compression rate.     

基于正交小波包的图像压缩算法

本文提出了一种新的图像压缩方法,深入研究了正交小波包在图像压缩中的分解与重构算法,详细介绍了正交小波最优基的选取,并应用MATLAB软件进行仿真实验.仿真结果显示,该方法压缩比大,信息损失小,能够较好恢复原有图像.     

基于模糊剪枝的最优小波包基图像压缩算法

提出一种基于模糊剪枝的最优小波包基图像压缩算法。图像经完全小波包分解后,以每个小波包分解的高频系数占所有高频系数的比值及每个节点子女熵值与原节点熵值的比值作为模糊系统的2个输入;通过构造模糊规则,将小波包系数分为裁剪类和保留类,由保留类构造最优小波包基;根据最优小波包基,在不同的小波包分解层次上采用不同的阈值对图像进行压缩。仿真实验结果表明,该算法与比特-失真率优化算法和小波包最优树全局阈值压缩算法相比性能更优。     

基于小波包分解的 SAR图像压缩 *

针对 SAR图像含有丰富的中、高频信息 ,而基于小波变换的图像压缩方法会丢失高频细节信息 ,提出了基于小波包分解的 SAR图像编码算法。小波包变换对 SAR图像进行完全分解 ,再用与后续编码器相关联的代价函数进行最佳基搜索 ,然后根据各子带小波包系数的重要性进行加权 ,采用多级树集合分裂算法 ( SPIHT)编码。实验结果表明 ,该算法更好地保留了 SAR图像的细节信息 ,获得了同压缩比下优于传统 SPIHT算法的编码性能 ,更有利于后续图像处理。     

基于小波分析的一种自适应图像压缩编码

文章在研究近年来基于小波变换的图像压缩编码方法的基础上,提出了一种自适应的小波分解方案。即在一定的能量准则下,根据子图像的能量大小决定是否进行小波分解,并由此导出自适应小波分解树和可调压比方案,然后结合自适应小波分解树的特性,给出恰当的小波系数量化、比特分配以及各层子图像的编码方案。实验表明,该文算法广泛适用于不同特征的数字图像,在较高压缩比时仍有较好的重建图像质量,而且压缩比在一定范围内可以调节。在相近压缩比的情况下,主观视觉质量和峰值信噪比都明显优于JPEG标准。     

基于小波域奇异值分解的图像压缩算法

提出了基于小波域奇异值分解（SVD）的图像压缩算法,该算法通过对小波分解系数进行奇异值分解,进而对小波系数进行压缩,实现图像压缩。将该算法与图像奇异值分解直接压缩的算法进行了实验比较,实验结果表明,该算法较图像奇异值分解直接压缩的算法具有更好的性能,在同样压缩比的情况下能获得更高的信噪比。     

基于遥感图像压缩的最佳小波分解层数研究

分析遥感图像特点及小波基的性质,选取D9/7和D5/3小波基进行遥感图像小波变换,采用分层树集合分裂算法(SPIHT)进行遥感图像压缩,并从信息熵的角度分析不同分解层数对图像压缩效果的影响.通过实验,得到了遥感图像压缩小波变换的最佳分解层数.     

基于提升小波MRI图像自适应阈值去噪算法

分析了小波的消失矩特性对图像重构误差的影响,提出了利用提升算法提高双正交小波消失矩的改进算法。通过提升算法对传统小波提高消失矩,改善了小波的性能,使小波具有更好的振荡性,能够更好地捕捉图像的细节,从而提高了重构信号的精确度。根据磁共振图像的特点及其噪声的分布特性,提出了一种对小波系数进行分块处理的阈值去噪方法。通过对分解后每个层次上的各高频系数矩阵分为多个子矩阵分别进行不同阈值的选取,实现在不同的对比度区域选取不同的阈值的目的,从而使阈值的选取更具有自适应性。     

Fast adaptive wavelet for remote sensing image compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.     

基于小波分解和互信息的图像配准方法

根据小波分解和互信息测度的原理,提出一种快速的图像配准方法。首先,对原图像进行小波分解,在保证配准精度下对分解图像进行灰度压缩,以减少配准参数的计算量,并利用最大互信息准则和下降单纯形的搜索策略找到最优配准参数实现图像配准。实验结果表明,这种图像配准方法能在保证配准精度条件下,提高配准的速度。     

Transform Based Lossy Compression of Multispectral Images

We have composed compression methods, which are based on the one-, two- and three-dimensional wavelet transforms. In addition, clustering the spectra of the image is considered. These compression methods, combining compression in the spectral and spatial domains, are compared by using a dataset of 65 multispectral images. The experimental results show that, at low compression ratios, the best method is based on the three-dimensional wavelet transform, where the spatial and spectral dimensions are transformed simultaneously. At higher compression ratios, a high quality wavelet-based compression method in the spatial dimensions combined with the principal component analysis in the spectral dimension gives the best results. This means that components of compressed images can be used as features in pattern analysis applications. The selection of the compression parameters is image-dependent. The results also show that, for multispectral images, both spectral and spatial compression could be used, and for best results, optimal bit allocation in encoding should be found. The results from the methods suggested are compared to the results found in the literature.     

单层小波分解下图像行列压缩感知选择算法

在进行图像压缩感知时发现以行或列进行压缩感知所得到的图像重构后的峰值信噪比(PSNR)是不同的。为了提高图像压缩重构的质量,提出了单层小波分解下图像行列压缩感知的选择算法。该算法首先计算图像的行与列数据的相对方差的最大偏离值,选择较小者对应的行或列作为压缩感知的对象,然后对图像进行单层小波变换分解出高频系数,在高斯观测矩阵下,对这些系数按指定的行或列进行压缩感知,最后利用正交匹配追踪算法(OMP)分别恢复压缩感知下的高频系数,并通过小波逆变换得到经过行列压缩感知后的重构图像,实验结果证明了算法的准确性。     

A MS-GS VQ codebook design for wireless image communication usinggenetic algorithms

An image compression technique is proposed that attempts to achieve both robustness to transmission bit errors common to wireless image communication, as well as sufficient visual quality of the reconstructed images. Error robustness is achieved by using biorthogonal wavelet subband image coding with multistage gain-shape vector quantization (MS-GS VQ) which uses three stages of signal decomposition in an attempt to reduce the effect of transmission bit errors by distributing image information among many blocks. Good visual quality of the reconstructed images is obtained by applying genetic algorithms (GAs) to codebook generation to produce reconstruction capabilities that are superior to the conventional techniques. The proposed decomposition scheme also supports the use of GAs because decomposition reduces the problem size. Some simulations for evaluating the performance of the proposed coding scheme on both transmission bit errors and distortions of the reconstructed images are performed. Simulation results show that the proposed MS-GS VQ with good codebooks designed by GAs provides not only better robustness to transmission bit errors but also higher peak signal-to-noise ratio even under high bit error rate conditions     

基于变换域的条带噪声去除方法

为解决线扫描图像中的条带噪声干扰问题,提出了傅里叶变换与小波分解相结合的变换域条带噪声去除方法。首先对图像进行多尺度小波分解,将包含条带噪声的小波子带与包含图像信息的小波子带分离;然后对含有条带噪声的小波子带进行傅里叶变换,并对变换系数进行带阻滤波以消除条带噪声。利用实际采集的带有条带噪声的棉花异性纤维图像进行仿真实验,结果表明:傅里叶变换与小波分解相结合的方法,去噪效果明显优于单独使用傅里叶变换或小波分解的方法,既能有效地去除图像中的条带噪声,又能较好地保持图像的细节信息。     

用于EVS的改进小波变换图像融合算法

为增强小波变换图像融合算法的实时性,提高视觉增强系统(EVS)可见光图像与红外图像实时融合的效率,提出了一种基于矩阵QR分解和小波变换的图像融合算法.该算法对原始图像的像素矩阵进行QR分解,再利用正交矩阵的性质,根据小波变换图像融合算法对QR分解得到的上三角矩阵进行分解融合,利用QR分解得到的正交矩阵逆变换得到融合图像.实验结果表明,该算法能获得较好的实时性,同时保证较好的融合效果.     

基于二维经验模态分解的医学图像融合算法

提出了一种自适应的二维经验模态分解(bidimensional empirical mode decomposition,简称BEMD)医学图像融合算法.待融合的医学图像经过BEMD分解成二维的内蕴模函数(bidimensional intrinsic mode function,简称BIMF)和趋势图像.BIMF图像经过Hilbert-Huang变换提取图像特征,然后,图像分解的各部分数据在区域融合规则下形成综合BEMD表示.最后,综合BEMD表示进行BEMD逆变换得到融合后的医学图像.BEMD分解方法是一种完全自适应的数据分解表达形式,具有比Fourier变化和小波分解更好的特性.该医学图像融合算法不需要预先定义滤波器或小波函数.实验结果表明,该算法与传统融合算法相比性能优越,能够大幅度提高融合图像的质量.     

Image subband coding using fuzzy inference and adaptive quantization

Wavelet image decomposition generates a hierarchical data structure to represent an image. Recently, a new class of image compression algorithms has been developed for exploiting dependencies between the hierarchical wavelet coefficients using zerotrees. This paper deals with a fuzzy inference filter for image entropy coding by choosing significant coefficients and zerotree roots in the higher frequency wavelet subbands. Moreover, an adaptive quantization is proposed to improve the coding performance. Evaluating with the standard images, the proposed approaches are comparable or superior to most state-of-the-art coders. Based on the fuzzy energy judgment, the proposed approaches can achieve an excellent performance on the combination applications of image compression and watermarking.     

基于改进型WBCT的多光谱图像融合方法

针对多光谱图像的融合问题,以提升小波变换代替传统小波变换,提出一种改进的WBCT方法。利用该方法对源图像进行多尺度分解,得到高频和低频分量,采用不同的融合规则对其进行处理,得到融合后的系数,并通过逆变换得到融合图像。实验结果表明,与Contourlet变换和提升小波变换相比,改进方法的融合效果有明显提升。     

基于离散小波的数字水印在图像认证中的应用

该论文提出了一种新的水印方案来认证数字图像。即使图像已被基本图像处理操作退化,该方案也可以检测到对图像的恶意篡改。该文的系统基于小波包系数的量化,并在保持良好视觉不可见性的同时利用人类视觉系统的特征来最大化嵌入量。该文开发了一个图像相关的方法,它在离散小波域计算最优量化阶。多分辨率离散小波分解的性质使你们可以在空域和频域定位图像的篡改。实验结果显示该系统能够检测图像未授权改动,同时对图像压缩稳健。     

基于矢量量化的嵌入式零树小波改进编码方法

基于矢量量化的SOFM算法和嵌入式零树小波算法(EZW)如今已经广泛的被使用于图像压缩领域,均被认为是非常有效的压缩编码技术。矢量量化方法压缩比较高,但往往存在分块效应,而EZW算法在高压缩比情况下存在恢复的图像质量较差的问题,提出一种基于矢量量化的嵌入式零树小波方法,它的基本原理是引入差值图像思想,先对原图像做矢量量化,再将原图像与矢量量化的恢复图像求差值图像,差值图像经过小波分解以后会存在大量为0的小波系数,再利用EZW编码,帮助提高重建图像质量。实验表明,相对于EZW和JPEG2000算法,本文算法的压缩比和编码质量均有显著提高。