Image edge detection based on nonsubsampled contourlet transform and mathematical morphology

A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled contourlet transform into multi-scale and multi-directional subbands. Then the edges in the high-frequency and low-frequency sub-bands are respectively extracted by the dualthreshold modulus maxima method and the mathematical morphology operator. Finally, the edges from the high- frequency and low-frequency sub-bands are integrated to the edges of the source image, which are refined, and isolated points are excluded to achieve the edges of the source image. The simulation results show that the proposed algorithm can effectively suppress noise, eliminate pseudo-edges and overcome the adverse effects caused by uneven illumination to a certain extent. Compared with the traditional methods such as LoG, Sobel, and Carmy operators and the modulus maxima algorithm, the proposed method can maintain sufficient positioning accuracy and edge details, and it can also make an improvement in the completeness, smoothness and clearness of the outline.

Fusion Algorithm for Remote Sensing Images Based on Nonsubsampled Contourlet Transform

就图象的人的视觉系统和特征而言，新奇图象熔化策略为在 nonsubsampled contourlet 的高分辨率图象和 multispectral 图象转变的 panchro matic (NSCT ) 被介绍域。NSCT 能由它好 multiresolution，移动不变性，和高 directionality 的特征的优点在图象给 asymptotic 边和轮廓的最佳的表示。紧张部件增加策略基于 LHS 变换被介绍进 NSCT 领域保存空间分辨率和颜色内容。熔化方法建议了的实验表演能改进空间分辨率并且保留光谱信息同时，并且与传统的原则部件分析( PCA )相比在视觉效果和量的肛门 ysis 两个都有改进方法， intensity-hue-saturation (代表耶稣之符号)变换技术，小浪变换加权的熔化方法，相应小浪基于变换的熔化方法，和 contourlet 基于变换的熔化方法。

Directional Filter for SAR Images Based on Nonsubsampled Contourlet Transform and Immune Clonal Selection

A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

Fast Algorithm for Nonsubsampled Contourlet Transform

多尺度的几何分析(MGA ) 为图象处理作为有效策略被认出了。作为 MGA 的分离工具之一， nonsubsampled contourlet 变换(NSCT ) 广泛地被使用了图象降噪，图象熔化，图象改进，特征抽取等等。然而，处理表演由于它的高冗余性被限制，并且导致集中的计算效率。因此，它的快算法在实践被需要。在这份报纸，我们采用一个优化方向性的过滤器银行(DFB ) 并且把它嵌进 NSCT 当使重建的表演的损失细微时，显著地加速计算速度。试验性的结果证明重建的图象质量能满足人的视觉系统。而且，改进 NSCT 有速度关于若干次比传统的的。图象降噪上的试验性的结果也验证建议方法的可行性和效率。

基于Nonsubsampled Contourlet变换的虹膜特征提取与匹配

为了鲁棒地获得高品质的虹膜纹理特征矢量,提出了一种新的无参数滤波器堆与虹膜感兴趣区域获取技术相结合的特征矢量提取方法。分析了基于Nonsubsampled Contourle变换(NSCT)滤波器的结构与性能,给出了无参化NSCT滤波器的设计步骤和设计要点。提出了采用坐标变换、剪切、平移和组合的虹膜感兴趣区域预处理技术。用NSCT滤波器滤波该虹膜感兴趣区域来获得信息丰富且少干扰的虹膜图案的特征矢量。实验结果表明该方法是更为有效的。

基于NSCT及张量分解的遥感建筑边缘检测技术

常见的边缘检测及语义分割技术在遥感图像中获取的建筑物边缘不够精细,且不能反映出建筑物屋顶的细节信息。针对上述问题,提出基于非下采样轮廓波变换(nonsubsampled contourlet transform,NSCT)与张量分解相结合的边缘检测技术,以获取包括屋顶在内的建筑物细节边缘特征。首先,利用NSCT进行图像分解,得到不同尺度和角度的精细子带系数;其次,对子带系数进行位置编码,得到相应位置的二阶对称张量,再将同一位置的不同尺度、不同角度的张量进行加权求和完成特征融合;最后,根据谱理论进行张量分解,得到图像的边缘特征。实验结果表明:与双向级联网络(bi-directional cascade network,BDCN)等5种方法相比,所提方法的峰值信噪比(peak signal-to-noise ratio,PSNR)和结构相似性(structural similarity,SSIM)指标均优于其他方法,其中PSNR和SSIM较基于深度学习的BDCN方法分别提升1.20和0.03,且检测结果能够更准确细致地反映出建筑物的边界及屋顶的边缘信息,可对建筑物的类型和风格研判提供更好的支持。

基于Nonsubsampled Contourlet变换的SAR图像形状特征检索

Nonsubsampled Contourlet变换是一种非抽取得具有平移不变性的多尺度多方向的变换。将Canny算子和Nonsubsampled Contourlet变换(NSCT)[1]相结合,对图像运用Canny算子[2]提取边缘特征,再进行Nonsubsampled Contourlet变换,引入了三阶中心矩作为特征向量提取形状特征的算法。实现了基于Nonsubsampled Contourlet变换的图像形状特征检索,并将结果与基于2-D小波变换和基于Contourlet变换的图像形状特征检索作了比较,实验结果证明该方法的图像形状特征检索效率有较大的提高。

Digital watermarking algorithm based on scale-invariant feature regions in non-subsampled contourlet transform domain

Contraposing the need of the robust digital watermark for the copyright protection field, a new digital watermarking algorithm in the non-subsampled contourlet transform （NSCT） domain is proposed. The largest energy sub-band after NSCT is selected to embed watermark. The watermark is embedded into scaleinvariant feature transform （SIFT） regions. During embedding, the initial region is divided into some cirque sub-regions with the same area, and each watermark bit is embedded into one sub-region. Extensive simulation results and comparisons show that the algorithm gets a good trade-off of invisibility, robustness and capacity, thus obtaining good quality of the image while being able to effectively resist common image processing, and geometric and combo attacks, and normalized similarity is almost all reached.

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, QAB/F as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, QAB/Fis 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images.

Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

Medical Image Fusion is the synthesizing technology for fusing multi-modal medical information using mathematical procedures to generate better visual on the image content and high-quality image output.Medical image fusion represents an indispensible role infixing major solutions for the complicated medical predicaments,while the recent research results have an enhanced affinity towards the preservation of medical image details,leaving color distortion and halo artifacts to remain unaddressed.This paper proposes a novel method of fusing Computer Tomography(CT)and Magnetic Resonance Imaging(MRI)using a hybrid model of Non Sub-sampled Contourlet Transform(NSCT)and Joint Sparse Representation(JSR).This model gratifies the need for precise integration of medical images of different modalities,which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly.In the proposed model,the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method.JSR is exercised to extricate the common features of the medical image for the fusion process.The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient,provides better results,and a high level of distinctness by integrating the advantages of complementary images.The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.

NSCT和PCNN相结合的遥感图像全色锐化算法

针对传统的全色锐化融合中细节信息提取不准确、融合光谱精度不高等问题,结合非下采样轮廓波变换(nonsubsampled Contourlet transform,NSCT)的多尺度多方向分解特点和脉冲耦合神经网络(pulse coupled neural networks,PCNN)脉冲同步发放特性等优点,提出了一种基于NSCT和PCNN的遥感图像全色锐化算法。该算法首先采用NSCT变换提取全色图像细节特征,然后将该特征注入PCNN模型获得的非规则分割区域,最终采用统计加权方式获取高分辨率的多光谱遥感图像锐化融合结果。采用WorldView-2和GF-2高空间分辨率遥感图像数据集实验结果表明,该算法在细节保持和光谱一致性等评价指标上均优于其他遥感图像融合算法,验证了该算法有效性。

基于NSCT与区域点火PCNN的医学图像融合方法

为了进一步改善医学图像融合质量,提出一种基于NSCT(非下采样Contourlet变换)与区域点火PCNN(脉冲耦合神经网络)的医学图像融合方法。该方法在低频子带系数采用基于区域点火PCNN的融合规则,应用PCNN改进的简化模型,将低频子带系数作为信号激励PCNN网络,利用点火区域强度分析区域点火特性,根据区域点火特性确定低频子带融合系数;在选择带通方向子带系数时,充分利用非下采样Contourlet变换的方向特性,采用改进的拉普拉斯能量作为带通方向子带系数的融合规则。实验结果表明,该方法与传统融合方法相比,能够较好的保留图像的边缘和过渡区域信息,大幅度提高融合图像的质量。

采用非采样Contourlet变换与区域分类的红外和可见光图像融合

提出基于多尺度变换和区域相结合的红外与可见光图像融合方法,用于有效保留红外图像与可见光图像中的空间信息及热目标信息,提升融合图像的可观测性和可理解性。首先,基于非采样Contourlet变换(NSCT)方法对红外和可见光图像进行初步融合,采用基于局部能量的规则融合低通子带系数,根据尺度内各方向子带的相关性原则融合带通方向子带系数。然后,计算初次融合后所得的融合图像与源图像的结构相似性(SSIM),根据源图像与初次融合图像的结构相似程度对图像进行区域分类,得到相似区域分类标识图。最后,依据区域内各自的相似度特性,分别采用不同的融合策略进行二次融合,从而得到最终的融合结果。实验结果表明:该方法能够充分提取源图像的区域特征和纹理特征,融合结果在主观和客观评价上均优于目前流行的融合方法。与仅使用NSCT法进行融合相比,实验所采用的两组图像的质量评价指标分别提高了16%、85%、54%、36%和18%、102%、84%、41%。表明该方法在主客观评价上均优于双树复杂小波变换(DTCWT)、NSCT、冗余离散小波变换(RDWT)等方法。

基于NSCT的红外与可见光图像融合

针对红外与可见光图像的不同特点,提出一种基于非采样Contourlet变换(NSCT)的红外与可见光图像融合算法。采用NSCT对源图像进行多尺度、多方向分解;分别采用基于局部能量和区域特征的融合规则得到融合图像的低频子带系数和带通方向子带系数;最后经过NSCT逆变换得到融合图像。实验结果表明,该算法能够获得较理想的融合图像,其融合效果优于基于Contourlet变换的图像融合算法。

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Laplacian方法、小波方法和传统的NSCT方法。

NSCT域内基于改进PCNN和区域能量的多光谱和全色图像融合方法

针对多光谱和全色图像的融合,提出了一种NSCT域内基于改进脉冲耦合神经网络(PCNN)和区域能量的融合方法。首先,利用NSCT将图像分解为一个低频子带和多个不同方向的带通子带。然后,对分解后的低频子带采用基于区域能量的自适应加权算法进行融合;在带通方向子带,结合改进的脉冲耦合神经网络,使用带通方向子带系数作为PCNN的外部输入激励,经过PCNN点火获得待融合图像的点火映射图,根据点火时间计算点火映射图的区域能量,通过判决算子选择待融合图像的带通方向子带系数作为融合系数。最后,对融合处理后的NSCT变换系数进行重构生成融合图像。实验结果显示:在迭代次数为100次时,与改进小波算法相比,标准差提高了9.48%,熵提高了0.95%,相关系数提高了21.56%,偏差指数降低了29.66%;与Contourlet算法相比,标准差提高了9.73%,熵提高了0.94%,相关系数提高了11.27%,偏差指数降低了9.45%;与NSCT算法相比,标准差提高了3.84%,熵提高了3.34%,相关系数提高了7.89%,偏差指数降低了7.42%。

基于NSCT域边缘检测的侧扫声呐图像分割新方法

针对侧扫声呐图像混响噪声严重、对比度低、分割困难的问题,根据侧扫声呐图像成像特点,提出一种基于非下采样Contourlet变换(NSCT)域边缘检测和区域生长的侧扫声呐图像分割新方法。首先,对侧扫声呐图像进行NSCT分解,通过K均值聚类法在NSCT域低频部分对阴影进行分割;然后通过寻找NSCT域高频层由同一粗尺度分解的2个细尺度相邻子带系数差的模极大值位置,来选择图像边缘点,并进行尺度内、尺度间的边缘融合;最后,利用基于边缘的区域生长方法完成对目标的分割。实验结果表明,该方法对侧扫声呐图像分割具有抗噪性能好、正确分类率高以及边缘定位准确等优点。

基于非抽样Contourlet变换的自适应阈值图像增强算法

提出了一种基于非抽样Contourlet变换的自适应阈值图像增强算法,首先对图像进行非抽样Contourlet变换得到不同尺度不同方向上的变换系数,然后由变换系数自适应地确定阈值和调整增强函数,并对变换系数做增强处理,最后对增强处理后的变换系数进行反变换,实现图像增强.实验结果表明,与其他基于变换域的算法相比,该算法可以得到更好的增强效果.

采用改进投影梯度非负矩阵分解和非采样Contourlet变换的图像融合方法

针对非负矩阵分解(NMF)算法时间复杂度较高,而投影梯度(PG)优化方法可以大幅降低NMF约束优化迭代问题的时间复杂度,提出一种基于改进的投影梯度NMF(IPGNMF)和非采样Contourlet变换(NSCT)相结合的图像融合方法。采用NSCT对已配准的源图像进行多尺度、多方向的分解,将分解后的低频部分作为原始数据,利用IPGNMF得到包含特征基的低通子带系数;高频部分应用了一种基于邻域一致性测度(NHM)的局部自适应融合规则得到各带通方向子带系数。经过NSCT逆变换得到融合图像。实验结果表明,融合结果在主观和客观评价上均优于NSWT方法、IPGNMF方法和NSCT方法。与NSCT法相比,实验所采用的两组图像的信息熵、清晰度和Q指标分别提高了0.0627%、0.901%、3.120 1%和2.769%、2.203%、1.049%。