基于无抽样Contourlet变换的图像增强方法

为了有效增强图像的细节信息,研究了基于无抽样Contourlet变换的图象增强方法。首先将待增强图像进行无抽样Contourlet变换,然后使用映射函数对无抽样Contourlet系数进行增强处理,最后进行无抽样Contourlet反变换得到增强后的图像。实验结果表明,该方法可以有效增强图像的微弱边缘信息。     

基于非抽样复轮廓波变换的图像去噪算法研究

将二维双树复小波变换(DT-CWT)与非抽样方向滤波器组(NSDFB)相结合,构造一种新的非抽样复轮廓波变换(NSCCT),并对其平移不变性作了相应证明。同时利用对称的正态逆高斯(NIG)分布先验概率模型和贝叶斯最小均方算法,提出一种基于NSCCT的图像去噪算法。实验结果表明,本文构造的NSCCT能够有效地抑制伪Gibbs现象,并且具有更丰富的方向分量,因而在图像的细节和纹理表现方面有一定的优势。     

一种新的基于非下采样Contourlet变换的图像去噪方法

提出了一种新的结合非下采样Contourlet变换(NSCT)和高斯比例混合模型的图像去噪方法.采取的主要方法为:1)通过NSCT对图像进行分解;2)根据高斯比例混合模型建立图像模型;3)利用贝叶斯估计进行图像去噪.实验结果表明,相对于已有算法,本文方法降噪效果好,在去噪性能指标和边缘保持的主观视觉上都表现出优异的性能.     

基于NSCT和SVM的脑MRI医学图像识别

利用NSCT变换具有多尺度和平移不变性,能够稀疏地表示纹理图像的特点,将具有丰富纹理信息的人体脑部核磁共振(MR)图像,从空间域变换到频率域表示。提取变换后表征图像特性的低频子带均值、方差及高频16个方向子带能量作为特征向量,输入SVM分类器进行分类识别。实验结果表明该方法对非病变脑部MR图像识别准确率达到100%,病变脑部MR图像的识别率达到90.90%,综合识别率达到95.45%。且该方法提取的特征维数少,识别速度快,识别率高,能够快速区分病变与非病变脑部MR图像。     

基于非下采样轮廓小波变换的图像去噪

针对采用下采样滤波器结构的轮廓波、轮廓小波在图像去噪过程中会引入伪吉布斯现象,利用小波变换(WT)和非下采样方向滤波器组(NDFB)构造了一种新的多尺度、多分辨率图像的非下采样轮廓小波变换(NWCT)。WT去除了拉普拉斯金字塔滤波器(LPF)的计算冗余,NDFB保证了该变换具有平移不变性。为了验证该变换的有效性,对其进行了图像去噪实验。实验结果表明,所提出方法能获得比WT、轮廓波变换(CT)、轮廓小波变换(WCT)更高的峰值信噪比(PSNR),并且能够很好地抑制伪吉布斯现象。     

非抽样轮廓波变换域图像融合方法研究

轮廓波变换由于不具备平移不变性,应用于图像融合会产生褶皱现象。针对轮廓波变换的这一不足,提出了一种基于非抽样轮廓波变换的图像融合算法。首先由非抽样金字塔和非抽样方向滤波器组对源图像进行多尺度、多方向分解,然后对得到的变换系数进行融合处理得到融合系数,最后对融合系数进行重构即可得到融合图像。算法将具备平移不变性的非抽样轮廓波变换应用于图像融合,消除了褶皱现象,获得了更好的融合效果。实验结果表明,与其他基于多分辨率分解的融合算法相比,该算法显著减小了融合图像的MSE及ΔH,提高了Correlation值及融合图像的清晰度,是一种有效的融合算法。     

Robust feature points extraction for image registration based on the nonsubsampled contourlet transform

In this letter, a new feature points extraction method based on the nonsubsampled contourlet transform (NSCT) is proposed for image registration. The primary motivation of this work is to determine the effectiveness of the NSCT transform in extracting feature points for image registration. Preliminary experimental results show that the registration accuracy and robustness of the proposed algorithm is acceptable and very promising, and confirm the success of the proposed NSCT-based feature points extraction approach.     

基于非亚采样轮廓波的舰船尾迹检测

介绍了海洋表面合成孔径雷达（SAR）图像的舰船尾迹检测算法。该算法首先对图像进行对数变换，将乘性噪声转化为加性噪声，然后对图像进行非亚采样contourlet 变换，根据变换后的像素均值对变换系数进行调整，以便去除噪声和提取尾迹轮廓。接着对经非亚采样contoulet调整后的图像进行局部Radon变换，即在对像素积分过程中沿着线性特征被分割后的若干短线段进行积分，而非对整幅图像进行积分。最后通过采用形态学膨胀和腐蚀的方法将临近的分离短线段连接起来便于后续的处理。采用该算法对实际SAR图像进行舰船尾迹检测，结果表明：该算法在背景噪声很强时仍能检测出略微有弯曲的尾迹信号。     

基于非亚采样轮廓波的舰船尾迹检测

介绍了海洋表面合成孔径雷达(SAR)图像的舰船尾迹检测算法.该算法首先对图像进行对数变换,将乘性噪声转化为加性噪声,然后对图像进行非亚采样contourlet变换,根据变换后的像素均值对变换系数进行调整,以便去除噪声和提取尾迹轮廓.接着对经非亚采样contoulet调整后的图像进行局部Radon变换,即在对像素积分过程中沿着线性特征被分割后的若干短线段进行积分,而非对整幅图像进行积分.最后通过采用形态学膨胀和腐蚀的方法将临近的分离短线段连接起来便于后续的处理.采用该算法对实际SAR图像进行舰船尾迹检测,结果表明:该算法在背景噪声很强时仍能检测出略微有弯曲的尾迹信号.     

Despeckling SAR images based on a new probabilistic model in nonsubsampled contourlet transform domain

A method for synthetic aperture radar (SAR) image despeckling based on a probabilistic generative model in nonsubsampled contourlet transform (NSCT) domain was proposed. The shrinkage estimator in NSCT domain consists of a new type of likelihood ratio and prior ratio, both of which are dependent on the estimated masks for the NSCT coefficients. While the previous probabilistic approaches are restricted to parametric models, the limitation is eliminated and the hybrid density model is applied in this paper. The suggested approach does not make heavy assumptions on the NSCT coefficient distribution, so that it can handle complex NSCT coefficient structures. The likelihood ratio is composed of the hybrid density, and the prior ratio is equipped with the selective neighborhood systems to enhance the detail information. The method can effectively adapt the shrinkage estimator to the redundancy property of the NSCT. The proposed approach was applied to real SAR images despeckling and compared through the SAR image vision effect, the equivalent number of looks, and the edge sustain index. Experimental results show that the proposed approach outperforms previous works involved in the paper with the better despeckling result and edge preservation.     

非降采样Contourlet图像边缘检测算法

介绍了非降采样Contoudet变换(NSCT)的结构特点与系数分布特性,并提出了基于NSCT的图像边缘检测算法,实现了对图像高频边缘的有效去噪和提取.该方法具有多向选择性、高低频信息并行检测、信息利用率高、两类边缘相互补偿修正精确定位及抗噪声干扰能力强等特点.通过仿真实验并与Prewitt、Sobel、Canny及多尺度小波边缘检测算法进行比较,验证了该方法的有效性和优越性.     

改进非下采样轮廓波在散斑条纹中的滤波处理

为了改进传统多尺度变换滤波在电子散斑干涉（ESPI）条纹图中去噪效果和边缘细节保护不理想问题，提出改进非下采样轮廓波（NSCT）滤波算法。采用离散平稳小波变换和NSCT变换模型，联合非线性扩散和改进的快速非局部均值滤波算法，进行了理论分析和实验验证，取得了将本文中算法应用于模拟和实验ESPI条纹图滤波效果定量分析的数据。结果表明，本文中的算法在模拟ESPI条纹图和实验图相比其它算法散斑指数最小分别为0.41121，0.38043，0.35362，对应峰值信噪比最大；该算法在提升去噪能力的同时，能够更好地恢复条纹细节信息。研究结果为以后应用多尺度变换滤波在ESPI条纹图打下了基础。     

The nonsubsampled contourlet transform: theory, design, and applications.

In this paper, we develop the nonsubsampled contourlet transform (NSCT) and study its applications. The construction proposed in this paper is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks. The result is a flexible multiscale, multidirection, and shift-invariant image decomposition that can be efficiently implemented via the à trous algorithm. At the core of the proposed scheme is the nonseparable two-channel nonsubsampled filter bank (NSFB). We exploit the less stringent design condition of the NSFB to design filters that lead to a NSCT with better frequency selectivity and regularity when compared to the contourlet transform. We propose a design framework based on the mapping approach, that allows for a fast implementation based on a lifting or ladder structure, and only uses one-dimensional filtering in some cases. In addition, our design ensures that the corresponding frame elements are regular, symmetric, and the frame is close to a tight one. We assess the performance of the NSCT in image denoising and enhancement applications. In both applications the NSCT compares favorably to other existing methods in the literature.     

Enhancement of blood vessels in retinal imaging using the nonsubsampled contourlet transform

This paper presents an enhancement method for blood vessels in retinal images based on the nonsubsampled contourlet transform (NSCT). The NSCT is a shift-invariant version of the contourlet transform built upon the nonsubsampled pyramid filter banks and the nonsubsampled directional filter banks. The proposed method uses the NSCT to decompose the input retinal image into eight directions from coarser to finer scales, and then analyzes and classifies the image pixels into three categories: vessel, uncertainty, and non-vessel pixels, according to the NSCT coefficients. Then, we modify the NSCT coefficients according to the class of each pixel using a nonlinear mapping function, and reconstruct the enhanced image from the modified NSCT coefficients. The experimental results show that the proposed method can obviously increase the contrast of retinal vessels and thus outperform other enhancement methods.     

基于Contourlet变换的人脸图像超分辨率技术研究

采用Contourlet变换,对基于学习的人脸图像超分辨率技术进行了研究.为了表示人脸特征,首先建立了Contourlet金字塔,针对Contourlet变换特征存在的不足,采用Contoudet变换的方向滤波器(DFB)对高斯金字塔的一阶和二阶梯度特征进行方向滤波,提取滤波后的特征作为新增加的特征.然后针对人脸的特殊性,在匹配过程中采用对应点进行匹配的方法,既保持了匹配的准确性又加快了匹配速度.最后采用最小欧式距离进行最优匹配的选择,对该距离进行判断.针对不同的距离采用不同的复原方法.实验结果表明,本文算法复原出的超分辨率人脸图像具有更好的视觉效果,更逼真、更接近于原始高分辨率图像.     

Digital autofocusing method based on contourlet transform

The autofocusing technique based on contourlet transform is discussed in this paper and an autofocusing method is proposed for images with much information in certain directions. The experimental results show that the proposed method can focus accurately and the sensitivity ratio is higher than that of the other autofocusing methods based on conventional image processing     

基于非下采样Contourlet变换的图像相关去噪

针对Contourlet变换不具有平移不变性,在图像去噪中容易产生伪吉布斯现象的情况,本文将具有平移不变性的非下采样Contourlet变换与相关去噪法相结合,采用不同的相关系数归一化方法,用Bayesian阈值代替传统的硬阈值来达到更好的去噪效果。实验表明,该方法提高了去噪后图像的峰值信噪比(PSNR),同时,有效保留了图像的纹理信息,避免了伪吉布斯现象,改善了图像的视觉效果。     

基于Contourlet变换的稳健性图像水印算法

提出了基于Contourlet变换的数字图像水印算法。与小波变换不同的是,Contourlet变换采用类似于线段(contoursegment)的基得到一种多分辨、局部化、方向性的图像表示。水印信号通过基于内容的乘性方案加载到Contourlet变换系数。在采用零均值广义高斯分布拟合Contourlet变换系数的基础上,提出采用极大似然估计实现水印的盲检测。依据Neyman-Pearson准则,在给定虚警率的情况下对判决准则进行了优化。实验结果表明在保证水印隐蔽性的前提下,水印对常见的信号处理手段以及几何变换具有很好的稳健性。     

基于改进稳态匹配概率的立体匹配算法研究

针对稳态匹配概率(Steady-State Matching Probability,SSMP)立体匹配算法在处理视差范围大的测试图中产生的空洞现象以及使用该算法后由于右视差图中的错误视差导致的左视差图中正确视差丢失问题,提出一种基于稳态匹配概率和半全局匹配(Semi-Global Matching,SGM)相结合的立体匹配算法。首先使用SSMP算法求取初始视差图。接着,使用基于爬山法颜色分割的填充准则进行填充。然后使用SGM算法重新获取视差图,将两幅视差图中一致的视差信息填充到经过左右一致性检测后的含有空洞的视差图中。最后,使用SSMP算法中的空洞填充和中值滤波得到精化后的视差图。实验结果表明,改进后的SSMP算法在Middlebury测试平台上第2版本的四组图像的平均匹配误差从5.38%减少到5.23%,第3版本部分测试图像的平均匹配误差从24.7%减少到21.5%,该算法能很好地处理上述问题,有效提高匹配精确度,且具有鲁棒性。