Undecimated double density wavelet transform based speckle reduction in SAR images

This paper describes an efficient and adaptive method of threshold estimation for removing Speckle noise from Synthetic Aperture Radar (SAR) images, based on Undecimated Double Density Wavelet Transform (UDDWT). Here the performance of image denoising algorithm is well improved by fixing different optimum threshold values for each wavelet coefficient. The choice of the estimation of the threshold value is carried out by analyzing the statistical parameters of the wavelet subband coefficients like Arithmetic Mean, Geometric Mean and Standard Deviation. Here the image is first decomposed into many subbands using UDDWT. Then based upon the statistical parameters of the wavelet coefficients of subbands, threshold values are found out for each wavelet coefficients. This threshold value is used in Soft Thresholding Technique to remove the noisy wavelet coefficients. Then the inverse transform is applied to get the denoised image. Evaluation parameters like peak signal to noise ratio, standard deviation to mean ratio and Edge Preservation Factor have been used for evaluating the performance of the proposed technique quantitatively. Experimental results on several benchmark images by using the proposed method show that, the proposed method yields significantly superior image quality. Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.     

Aggressive region growing for speckle reduction in ultrasound images

Speckle appears in all conventional medical B-mode ultrasonic images and can be an undesirable property since it may mask small but diagnostically significant features. In this paper, an adaptive filtering algorithm is proposed for speckle reduction. It selects a filtering region size using an appropriately estimated homogeneity value for region growth. Homogeneous regions are processed with an arithmetic mean filter. Edge pixels are filtered using a nonlinear median filter. The performance of the proposed technique is compared to two other methods––the adaptive weighted median filter and the homogeneous region growing mean filter. Results of processed images show that the method proposed reduces speckle noise and preserves edge details effectively.     

基于冗余小波变换的医学超声图像去斑噪算法

医学超声图像中固有的斑点噪声严重降低了图像的可解译程度,影响了后续的图像分析和诊断。提出了一种基于冗余小波变换的超声图像去斑算法,首先对含斑图像进行对数变换,将乘性噪声变成加性噪声;再对转换后图像做冗余小波分解;在小波系数服从广义高斯分布的前提下,计算每个小波高频子带的贝叶斯萎缩阈值,利用软阈值方法修正小波系数。实验结果表明,该算法去斑性能优于传统的空间域滤波和正交小波阈值去噪方法。     

Finding out general tendencies in speckle noise reduction in ultrasound images

This article investigates and compiles some of the techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images. With this information, a comparison of all the methods studied is done based on an experiment, using quality metrics to test their performance and show the benefits each one can contribute. To test the methods, a synthetic, noise-free image of a kidney is created and later simulations using Field II program to corrupt it are performed. This way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference. Since real ultrasound images are already noise corrupted images and real noise-free images do not exist, conventional metrics cannot be used to indicate the quality obtained with filtering. Nevertheless, we propose the use of the tendencies observed in our study in real images.     

树状小波局部对比度与图像融合及性能评估

针对红外热成像中目标识别和跟踪的特点,提出了一种基于树状小波变换的局部对比度融合算法,首先采用多尺度树状小波变换的方法对已配准的源图像在相应的能量准则下进行分解,克服了小波变换的移变性;对于分解后的低频子图像采用加权平均的融合规则,对于高频子图像采用局部对比度量测的融合规则,融合图像既保持了源图像的细节信息,又滤去了红外成像中的各类噪声。采用交叉分辨力评价算子量测红外图像中目标与背景的衬比度;通过仿真及基于客观的图像融合评价标准,分别从信息熵、标准差、平均梯度和交叉熵四个参数对图像融合效果进行评估,证明了对于双波段红外辐射图像的融合,提出的融合算法优于小波融合算法、形态学区域分割融合算法。算法尤其适用于红外热成像系统的目标识别和跟踪。     

基于并行小规模卷积神经网络的图像质量评价

图像质量客观评价广泛应用在图像处理任务中,参考深度学习技术的研究成果,提出了一种基于并行小规模卷积神经网络的无参考图像质量评估算法。卷积操作和并行的多尺度输入能学习到丰富和细微的图像失真特征,首先利用高斯图像金字塔获取不同尺度的失真图像做为4路小规模单层卷积神经网络的输入,经过卷积和池化处理后,输出4路特征矢量,把学习到的特征矢量融合后,通过全连接回归映射为图像质量预测分数。参数优化分2个阶段完成,提高了模型精度。实验测试结果表明,设计的网络模型简单有效,提出的算法性能高于当前主流算法,具有很好的稳定性和较强的泛化能力。      

基于局部自相似性的图像质量度量

使用图像所包含的一种更本质和深层的特征——局部自相似性,并结合人眼观察图像时注意力由中央向周围递减这一特性,提出了一种基于局部自相似性的图像质量度量方法。通过与已有方法的对比实验表明,该方法能对不同类型的失真图像进行更有效的评判,评价结果能够更好地符合人眼主观视觉特性。     

基于小波的图像基本处理技术研究

鉴于小波多尺度分解与重构在图像的光照补偿、去噪、压缩方面的重要作用,介绍了小波变换的分解与重建,提出了包括IVIW光照补偿方法、基于Haar小波的去噪与压缩方法 ,并通过MATLAB R2009a平台对Yale人脸库中的图像进行成功验证。结果表明,基于小波变换的图像预处理方法简单易行且有效,解决了原始图像包含巨大数据量的问题,减少了计算的复杂度,尽可能消除了可变因素的影响,得到清晰的人脸图像。     

基于小波滤波的时间序列子序列聚类

针对时间序列子序列聚类存在的平凡相似和水平伸缩等问题,提出了一种新的子序列聚类算法。它采用多孔平滑滤波器组对时间序列进行低通平滑处理,在所得到的多个尺度序列上生成平凡簇,然后将各个平凡簇的代表子序列作为数据样本进行聚类。新方法利用平凡簇克服了子序列聚类中的平凡相似问题,并且可以在时间序列上发现不等长的相似子序列,较好地解决了水平轴伸缩问题。实验结果证明新算法对于子序列聚类具有比较好的效果。     

Fractional subpixel diffusion and fuzzy logic approach for ultrasound speckle reduction

Speckle is the dominant source of noise in ultrasound imaging and is a kind of multiplicative noise. It is difficult to design a filter to remove speckle effectively. In this paper, a novel fuzzy subpixel fractional partial difference (FSFPD) for ultrasound speckle reduction is proposed. Euler-Lagrange equation acts as an increasing function of the fractional derivative's absolute value of the image intensity function. The fractional order partial difference is computed in the frequency and fuzzy domain with subpixel precision. We test the proposed method on both synthetic and real breast ultrasound (BUS) images. The comparisons of the experimental results show that the proposed method can preserve edges and structural details of ultrasound images well while removing speckle noise. In addition, the filtered images are assessed and evaluated by radiologists using double blind method. The results demonstrate that the discrimination rate of breast cancers has been highly improved after employing the proposed method.     

Reversible robust data hiding based on wavelet filters modification

Multimedia Tools and Applications - In this paper, a new robust reversible data hiding method is proposed. The method is designed based on wavelet modifications which result in a scalable data...     

An optimized non-local LMMSE approach for speckle noise reduction of medical ultrasound images

In this paper, a modified linear-minimum-mean-square-error (LMMSE)-based estimator is presented to reduce speckle noise in ultrasound (US) medical images. In order to significantly improve the performance of the LMMSE estimator, we consider the data redundancy feature which naturally exists in the US images. Since the classical LMMSE method, due to the use of local statistics, cannot perform well in areas where the intensity variation is high, we exploit the similarity between pixels to resolve this problem. In this regards, by using characteristics of the second order local statistics and Pearson distance, an optimum set of similar pixels is selected to be used in the proposed LMMSE-based estimator. Therefore, a good balance between maintaining small details and reducing speckle noise in different regions of US images can be achieved. Quantitative and qualitative results on synthetic and real US data demonstrate that the proposed method yields competitive results in despeckling process compared to the state-of-the-art methods.

     

基于小波-Contourlet变换的图像增强算法

小波-Contourlet变换能够良好捕获图像的方向特征和边缘信息.利用小波-Contourlet变换良好的多尺度性和多方向性特征,提出一种基于小波-Contourlet变换的图像增强算法,用非线性增强算子对变换的各子带系数做增强处理.并引入了循环平移消除由于小波-Contourlet变换缺乏平移不变性而产生的图像失真.实验结果表明,该方法有效增强了图像的边缘细节和纹理特征.     

基于小波邻域信息量的图像融合

针对多模图像融合问题提出了一种基于小波变换的新方法.将小波低频系数乘以加权因子1/R,减少低频部分所占整个图像的信息比例,并采取绝对值取大的融合规则选取小波低频系数;使用由方差和平均梯度构造的新的评价因子--小波邻域信息量作为融合规则选取小波高频系数.实验结果表明,该方法得到的融合图像体现出更强的融合性能.     

基于小波系数相关性的图像隐写分析

提出了一种基于小波包分解和小波系数相关性的通用型图像隐写分析方法。对差分处理后的图像进行小波包分解,提取图像及其小波子带的高阶统计量作为特征。利用图像小波系数在尺度方向和空间方向的相关性,使用马尔可夫模型挖掘小波系数层内和层间相关性,提取转移概率矩阵作为特征。针对H4PGP、F5和OutGuess隐写算法的实验表明,方法对上述三种隐写算法具有较好的检测效果。     

小波和KPD的图像压缩方法

介绍了KPD的基本理论,提出了一种基于小波和KPD的图像编码方法。对图像进行Kronecker积分解,达到第一次压缩的目的。根据Kronecker积分解后的各个分解小块固有的性质,把这些小块分成两类：高频信息和低频信息。对于低频信息选取SPECK小波编码方式,而对于高频信息先进行小波分解,再对其中的混频信息进行编码,从而达到再次压缩的目的。实验证明该算法具有计算简便,压缩比较高的特点。     

基于小波域奇异值分解的图像拷贝检测

提出一种基于小波域奇异值分解（SVD）和早期融合技术的数字图像拷贝检测算法。这种基于内容的拷贝检测模式主要面向数字图像被动式取证和数字版权管理等领域。为了提高图像描述特征的效率,算法利用多尺度小波分析提取并融合具有图像全局和局部特征的多尺度奇异值特征向量。实验结果表明,该算法不仅在识别几何变换、信号处理、图像操作处理及组合变换等不同攻击下的图像修改版本时具有较强的鲁棒性和内容辨识性,而且具有较高的检测率。算法可以用于数据库或网络环境下的数字图像盗版检测。     

基于小波相位的噪声抑制方法研究

对弱小运动目标检测时,其图像信噪比很低,检测前需先进行噪声滤除.针对这一问题,提出一种基于单一尺度小波相位信息的噪声抑制新方法.利用同一尺度内噪声相位的突变性以及与邻近像素点相位的不连续性,将点相位系数与3×3窗口模板的窗相位系数之差和阈值做比较,判断是否为噪声.实验结果表明:图像信噪比得到显著提高,并较完整地保留了图像边缘和细节.     

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.     

基于整数小波变换的图像压缩算法

IWT（integer wavelet transform）是一种基于提升格式整数小波变换的图像压缩算法.IWT比传统的浮点小波变换效率要高.在硬件实现时,整数运算比浮点运算便宜,基于提升格式的整数分解时所需的存储空间只是传统变换的一半.基于此考虑有损压缩,图像先作基于提升格式的整数分解,然后结合改进的EZW和自适应量程编码.在编码性能不受影响的情况下,此算法比Shaprio的EZW编码要快1.5倍.