一种基于 Contourlet 变换的彩色图像全息水印算法

提出了一种结合了全息水印加密技术和 Contourlet 变换技术的彩色图像水印算法。 算法首先将 RGB 模式的水印载体图像转换至 YCrCb 颜色空间,选取亮度分量作为水印载体通道,对其进行多层 Contourlet 分解,得到细节子带图像;而后对二值水印图像进行傅里叶全息加密,将加密后的图像嵌入至载体图像的 Contourlet变换系数中。 仿真实验结果表明,该水印算法具有良好的不可见性,对于常见的几何变换及多种攻击具有良好的鲁棒性。     

一种基于双边假设检验的Contourlet域乘性水印盲检测算法

为解决Contourlet变换域中乘性水印的检测问题,根据广义高斯分布描述图像Contourlet变换高频方向子带系数的统计分布特征,在服从广义高斯分布的系数中,将水印的检测问题转化为了具有未知参数的确定性伪随机序列的检测问题,利用双边假设检验推导出了适用于Contourlet域乘性水印的盲检测器。实验表明,该盲检测器的判决准,对任何嵌入强度均有效,并且可以自适应地确定检测阈值。     

一种基于Contourlet-DFT的加密全息水印技术

目的提出一种新的、适用性强的加密全息水印技术。方法首先将载体图像进行一级Contourlet变换,得到其低频子带图像;其次将其低频子带图像进行离散傅里叶变换,得到频域的幅度谱和相位;最后将加密全息水印嵌入到其高频部分的幅度谱中,并对中心对称块相同的嵌入。结果文中算法水印隐蔽性好,重建水印图像视觉有意义;算法鲁棒性强,可以抵抗均值滤波、中值滤波和高斯滤波,对噪声、JPEG压缩攻击和裁切攻击有很强的抵抗能力,同时可以有效抵抗打印-扫描攻击。提取水印时,不需要原始图像的参与,属于盲水印算法。结论该方法能够满足数字图像版权保护的需要。     

基于Logistic混沌加密的NSCT-SVD抖动调制盲水印算法

目的提出一种既安全又具高稳健性的盲水印算法。方法首先通过Logistic映射混沌置乱对图像加密,然后对载体图像进行三层非采样Contourlet变换(NSCT),提取低频部分进行8×8分块,最后对低频带的子块进行奇异值分解(SVD),将混沌变换的水印图像嵌入在进行抖动调制的奇异值中。结果该算法解决了奇异值分解引起的False positive问题即虚警率,在提取水印的算法过程中没有用到原载体图像,实现了水印的嵌入及盲检测。实验结果显示,该算法提高了水印的防伪性,耐常见攻击的抵抗能力也较强。结论该算法满足了数字水印技术要求的隐蔽性和稳健性,并且可以对数字产品起到版权保护的作用。     

基于整数小波变换与矩阵分解的感兴趣区域可逆水印算法研究

目的针对传统感兴趣区域水印算法抵抗几何攻击能力较弱的缺陷,提出一种基于IWT-Schur的感兴趣区域可逆水印算法。方法首先对载体图像做小波变换,筛选出各子带ROI系数,接着采用Arnold变换加密水印图像,加密水印图像做整数小波变换得到一系列分量。最后结合Schur分解,将水印各分量对应加至载体各子带的ROI。结果采用整数小波变换IWT与矩阵Schur分解的嵌入方式,使得含水印图像的视觉质量良好,算法实现容易。含水印图像没有受到干扰时检测到的水印与原水印一致,含水印图像受到攻击时,也表现出良好的性能,检测到的水印品质较好。结论实验证明,此方案水印提取正确,且感兴趣区域无损恢复。     

基于QR码与混沌加密的Contourlet域彩色图像盲水印算法

目的针对现行数字水印算法中抗几何攻击能力弱以及嵌入水印信息容量差的问题,提出一种Contourlet变换下QR码与混沌加密相结合的彩色图像水印算法。方法采用QR码作为水印图像,利用Logistic混沌序列对其进行加密,再将加密的水印图像嵌入到二级Contourlet变换的低通子带中,利用系数间的大小关系,修改系数完成水印嵌入与提取。结果通过实验结果可知,水印的嵌入对载体图像影响较小,PSNR值高达42.764,经过旋转、剪切及常规攻击后,提取出的水印均可正确解码。结论该算法具有良好的安全性、不可见性,对于各类攻击具有较优的鲁棒性。水印信息容量高于普通图像,水印提取方式为盲提取,能够更好地满足各类数字作品版权保护的实际需要。     

基于奇异值分解的Contourlet域水印算法

目的提高数字水印算法抗信号处理攻击能力,提升水印鲁棒性。方法提出一种基于Contourlet变换和奇异值分解(SVD)相结合的水印算法。对水印图像进行多次Arnold置乱处理,实现置乱加密;选择Contourlet域变换系数的方向子带作为水印嵌入区域;在Contourlet系数构建矩阵中进行SVD分解,并结合人类视觉系统进行嵌入强度的自适应选择。结果实验结果得出图像的峰值信噪比为54.1279 d B,表明所述算法具有很好的透明性、隐蔽性。结论所述图像数字水印算法具有很好的鲁棒性、不可见性以及抵抗各种攻击的能力,对数字产品的版权保护具有促进作用。     

基于Tchebichef矩和Logistic混沌加密的数字水印算法

目的针对现有的基于图像矩的数字水印算法比较复杂、鲁棒性差,尤其在抵抗缩放和旋转等攻击方面性能较差的问题,提出一种基于Tchebichef矩的数字水印算法。方法首先将载体图像进行非下采样Contourlet变换,得到与载体图像大小相同的低频子带,将低频子带分块后对各子块进行奇异值分解(SVD),根据奇异值的特点选择最佳嵌入位置,并由左奇异值和右奇异值矩阵自适应地计算水印嵌入强度,将通过Logistic混沌加密后的水印嵌入选中子块的奇异值中。计算含水印图像的Tchebichef矩,用于旋转及缩放校正。结果实验结果显示,文中算法的不可见性好,提取的水印NC值达到0.9386,能够有效抵抗旋转和缩放攻击。结论该算法对任意角度的旋转攻击和缩放攻击具有很好的鲁棒性,能够达到保护数字产品的目的。     

基于NSCT和PCNN的红外与可见光图像融合方法

提出了一种基于非采样Contourlet变换(NSCT)和脉冲耦合神经网络(PCNN)的红外与可见光图像融合方法.首先用NSCT对已配准的源图像进行分解,得到低频子带系数和各带通子带系数;其次对低频子带系数采取一种基于边缘的方法以得到融合图像的低频子带系数;对各带通子带系数提出了一种改进的基于PCNN的图像融合方法来确定融合图像的各带通子带系数;最后经过NSCT逆变换得到融合图像.实验结果表明,本文方法优于Laplaeian方法、小波方法和传统的NSCT方法.     

小图像子集提取水印的小波域盲水印算法

提出一种新的盲水印算法,能从一个小图像子集上检测出水印,首先将图像进行小波变换,选择两个所需要的子带,利用相邻特征平均值和奇偶判决法在两个子带上各自重复内嵌一半水印,在水印检测过程中不需要原始图像。根据本算法的实验结果,该算法具有较好的透明性,对各种攻击有较强的顽健性,尤其是增强了小波域水印在面临裁剪攻击和联合攻击时的顽健性。     

应用轮廓波和稀疏编码收缩法消噪毫米波图像

结合以峭度为稀疏标准的稀疏编码算法的高阶统计特性以及轮廓波分解的方向性和能量变化特性,提出了一种新的基于轮廓波和稀疏编码收缩技术的毫米波图像消噪方法。稀疏编码是一种有效的模拟视觉系统的图像特征提取方法,根据提出的特征系数的稀疏先验分布知识,能够自适应地确定收缩阈值。把该收缩技术应用到轮廓波变换域,能够很好地减弱毫米波图像中的未知噪声。采用相对信噪比评判消噪图像的质量,仿真实验表明,与标准稀疏编码收缩方法、轮廓波变换域降噪方法以及小波软阈值收缩方法相比,该降噪方法能够获得较好的图像恢复质量。     

Contourlet变换和遗传算法相结合的沥青红外图像增强方法

沥青施工过程中,采集的红外图像容易受到周围环境噪声的影响,使图像变得模糊、信噪比低,从而导致后续图像处理分析的准确度降低。针对该噪声特性,提出了一种Contourlet变换和遗传算法相结合的红外图像增强方法。首先对原始红外图像进行Contourlet变换,得到带有多尺度、多方向信息的带通子带,然后对其进行模糊增强,并通过自适应遗传算法优化模糊增强参数,最后对增强后的带通子带进行Contourlet逆变换,得到效果增强的红外图像。实验结果表明,与其它几种常用的红外图像增强方法相比,此方法能更有效地抑制噪声,提高清晰度,取得了较好的增强效果。     

非亚采样Contourlet遥感图像融合

非亚采样 Contourlet 变换是在非亚采样塔型滤波器及非亚采样方向滤波器组的基础上建立起来的,它是一种移不变多方向多尺度图像表示方法.介绍了该变换的结构特点与系数分布特性,并研究了基于非亚采样Contourlet 变换的图像融合算法.该算法利用非亚采样Contourlet 的平移不变性以及NSCT 系数特点,有效准确地提取图像边缘与细节区域,并分别在高、低频域针对不同区域采用不同的融合方法,有效挖掘了待融合图像中的有效信息.这种具有多分辨率分析和多方向滤波特点的处理方法,提高了融合后遥感图像中的信息量和清晰度,对不同分辨率不同方向上的信息进行挖掘及融合,解决了传统小波融合算法中方向数目受限的不足.通过仿真实验与传统融合方法进行比较,验证了该方法的有效性和优越性.     

无下采样轮廓波变换纹理图像检索系统

针对基本轮廓波变换纹理检索系统检索率较低的问题,提出了一种无下采样轮廓波变换(NSCT)纹理图像检索系统.该系统采用的轮廓波变换由无下采样拉普拉斯金字塔级联无下采样方向滤波器构成,特征向量采用子带系数的能量和标准偏差连接而成;以Canberra距离为相似度度量标准.比较了基于同样架构的基本轮廓波变换和NSCT纹理检索系统的性能.实验结果表明:在特征向量长度,检索时间、所需存储空间基本相同的情况下,NSCT检索系统比基本轮廓波变换检索系统具有更高的检索率;NSCT分解结构参数以及图像类型对于平均检索率也有较大的影响.     

一种NSCT域多聚焦图像融合新方法

针对多聚焦图像融合存在的问题,提出一种基于非下采样Contourlet变换（NSCT）的多聚焦图像融合新方法。首先,采用NSCT对多聚焦图像进行分解;然后,对低频系数采用基于改进拉普拉斯能量和（SML）的视觉特征对比度进行融合,对高频系数采用基于二维Log-Gabor能量进行融合;最后,对得到的融合系数进行重构得到融合图像。实验结果表明,无论是运用视觉的主观评价,还是基于互信息、边缘信息保留值等客观评价标准,该文所提方法都优于传统的离散小波变换、平移不变离散小波变换、NSCT等融合方法。     

A novel brain image enhancement method based on nonsubsampled contourlet transform

In this article, a novel brain image enhancement approach based on nonsubsampled contourlet transform (NSCT) is proposed. First, the image is decomposed into a low‐frequency component and several high‐frequency components by the NSCT; Second, the gamma correction is applied to deal with the low‐frequency sub‐band coefficients, and the adaptive threshold is used to remove the noise of the high‐frequency sub‐bands coefficients; Third, the inverse nonsubsampled contourlet transform is adopted to reconstruct the processed coefficients; Finally, the unsharp filter is used to enhance the reconstructed image. The experimental results demonstrate that the performance of the proposed method is superior to the state‐of‐the‐art algorithms in terms of brain image enhancement.     

Multifocus noisy image fusion using contourlet transform

Abstract

Image fusion is a challenging area of research with a variety of applications. The process of image fusion collects information from different sources and combines them in a single composite image. The composite fused image can better describe the scene than any of the source images. In this paper, we have proposed a method for noisy image fusion in contourlet domain. The proposed method works equally well for fusion of noise free images. Contourlet transform is a multiscale, multidirectional transform with various aspect ratios. These properties make it more suitable for image fusion than other conventional transforms. In the proposed work, the fusion algorithm is combined with a denoising algorithm to reverse the effect of noise. In the proposed method, we have used a level dependent threshold that is based on standard deviation of contourlet coefficients, mean and median of the absolute contourlet coefficients. Experimental results demonstrate that the proposed method performs well in the presence of different types of noise. Performance of the proposed method is compared with principal components analysis and sharp fusion based methods as well as other fusion methods based on variants of wavelet transform like dual tree complex wavelet transform, discrete wavelet transform, lifting wavelet transform, multiwavelet transform, stationary wavelet transform and pyramid transform using six standard quantitative quality metrics (entropy, standard deviation, edge strength, fusion factor, sharpness and peak signal to noise ratio). The combined qualitative and quantitative evaluation of the experimental results shows that the proposed method performs better than other methods.     

Statistical correlative model in the multimodal fusion of brain images

Fusing multimodal medical images into an integrated image, providing more details and rich information thereby facilitating medical diagnosis and therapy. Most of the existing multiscale-based fusion methods ignore the correlations between the decomposition coefficients and lead to incomplete fusion results. A novel contextual hidden Markov model (CHMM) is proposed to construct the statistical model of contourlet coefficients. First, the pair brain images are decomposed into multiscale, multidirectional, and anisotropic subbands with a contourlet transform. Then the low-frequency components are fused with the choose-max rule. For the high-frequency coefficients, the CHMM is learned with the EM algorithm, and incorporate with a novel fuzzy entropy-based context, building the fuzzy relationships among these coefficients. Finally, the fused brain image is obtained by using the inverse contourlet transform. Fusion experiments on several multimodal brain images show the superiority of the proposed method in terms of both visual quality and some widely used objective measures.     

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques.