Wind turbine clutter mitigation using morphological component analysis with group sparsity

To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.

Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection results.In order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

Image Restoration Using Hybrid Features Improvement on Morphological Component Analysis

Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted image,including a block matching 3D(BM3D)method,an adaptive non-local mean(ANLM)scheme,and the K-singular value decomposition(K-SVD)algorithm.In the proposed method,we employ the morphological component analysis(MCA)to decompose an image into the texture,structure,and edge parts.Then,the BM3D method,ANLM scheme,and K-SVD algorithm are utilized to eliminate noise in the texture,structure,and edge parts of the image,respectively.Experimental results show that the proposed approach can effectively remove interference random noise in different parts;meanwhile,the deteriorated image is able to be reconstructed well.

Application of Decomposition and Denoising of Gearbox Signal Based on Morphological Component Analysis

Morphological component analysis( MCA) is a signal separation method based on signal morphological diversity and sparse representation. MCA can extract the signal components of different morphologies by different dictionary combinations. Firstly,the theory of MCA was analyzed with sparse representation principle and relaxation criterion. Then detailed steps of block coordinate relaxation( BCR) were given. Finally,algorithm performance was verified by simulation signals analysis, MCA was applied to decomposing and denoising gearbox signals, and the fault parameters were extracted by energy operator demodulation envelop of morphological component.

Seismic data denoising under the morphological component analysis framework combined with adaptive K-SVD and wave atoms dictionary

Many different effective reflection information are often contaminated by exterior and random noise which concealed in the seismic data.Traditional single or fixed transform is not suit for exploiting their complicated characteristics and attenuating the noise.Recent years,a novel method so-called morphological component analysis(MCA)is put forward to separate different geometrical components by amalgamating several irrelevance transforms.According to study the local singular and smooth linear components characteristics of seismic data,we propose a method of suppressing noise by integrating with the advantages of adaptive K-singular value decomposition(K-SVD)and wave atom dictionaries to depict the morphological features diversity of seismic signals.Numerical results indicate that our method can dramatically suppress the undesired noises,preserve the information of geologic body and geological structure and improve the signal-to-noise ratio of the data.We also demonstrate the superior performance of this approach by comparing with other novel dictionaries such as discrete cosine transform(DCT),undecimated discrete wavelet transform(UDWT),or curvelet transform,etc.This algorithm provides new ideas for data processing to advance quality and signal-to-noise ratio of seismic data.

Capability of discrete element method to investigate the macro-micro mechanical behaviours of granular soils considering different stress conditions and morphological gene mutation

Discrete element method(DEM)has been widely utilised to model the mechanical behaviours of granular materials.However,with simplified particle morphology or rheology-based rolling resistance models,DEM failed to describe some responses,such as the particle kinematics at the grain-scale and the principal stress ratio against axial strain at the macro-scale.This paper adopts a computed tomography(CT)-based DEM technique,including particle morphology data acquisition from micro-CT(mCT),spherical harmonic-based principal component analysis(SH-PCA)-based particle morphology reconstruction and DEM simulations,to investigate the capability of DEM with realistic particle morphology for modelling granular soils’micro-macro mechanical responses with a consideration of the initial packing state,the morphological gene mutation degree,and the confining stress condition.It is found that DEM with realistic particle morphology can reasonably reproduce granular materials’micro-macro mechanical behaviours,including the deviatoric stressevolumetric straineaxial strain response,critical state behaviour,particle kinematics,and shear band evolution.Meanwhile,the role of multiscale particle morphology in granular soils depends on the initial packing state and the confining stress condition.For the same granular soils,rougher particle surfaces with a denser initial packing state and a higher confining stress condition result in a higher degree of shear strain localisation.

基于多形态学成分分析的图像融合

将多尺度分解与稀疏表示相结合,提出了一种基于多形态学成分分析(MCA)的图像融合算法。采用基于联合稀疏表示(JSR)的方法融合卡通子图像中的冗余和互补信息,并利用基于方向特征的方法融合具有更多细节信息和噪声的纹理子图像。结果表明,提出的图像融合算法在主观视觉效果和客观评价指标上均优于先进的图像融合算法。

基于改进MCA的干涉高光谱图像分解

干涉高光谱图像特殊的成像原理,使其帧内存在着大幅值且位置固定的干涉条纹,而帧间存在着水平移位的背景图像,这种特点会严重的破坏原始图像的固有结构,从而导致新兴的压缩感知理论与传统压缩算法的直接应用无法得到理想的效果。由于干涉条纹信息与背景图像信息的特征不同,能够对干涉条纹与背景图像进行稀疏表示的正交基也是不同的。基于这种思想,使用MCA(morphological component analysis)算法对干涉高光谱图像中干涉条纹信息与背景图像信息进行分离处理。由于干涉高光谱图像数据量庞大,传统的MCA算法对干涉高光谱数据的图像分解,迭代收敛速度慢,运算效率较低,故而针对干涉高光谱数据特点对传统MCA算法进行改进,改变其迭代收敛条件,当分离后的图像信号与原始图像信号的误差已经基本保持不变时,即终止迭代;并根据对应正交基能且仅能稀疏表示对应信号的思想,对阈值采用自适应的方式进行更新,在新的阈值更新模式中,图像信号在不同正交基下的映射系数被计算与比较。大量实验结果表明,对于LASIS数据与LAMIS数据,MCA算法都能够较完美的将干涉高光谱图像分解,改进的MCA算法更能在保持完美分解输出结果的同时,相对于传统MCA方法显著的减小迭代次数,更快的达到迭代收敛条件,从而有效的提高了算法的运算效率与实时性需求,也为新兴的压缩感知理论在干涉高光谱图像中的进一步应用提供了一种很好的解决方案。

一种用于PCA与MCA的神经网络学习算法

主元分析（ＰＣＡ）和次元分析（ＭＣＡ）是用于特征提取、数据压缩、频率估计、曲线拟合等信号处理的基本技术．以神经网络来实现ＰＣＡ和ＭＣＡ是当今研究的一大热点，相关矩阵Ｒ的特征值重数不为１时的主、次元分析则是其中一大难题．本文提出了一种新的学习算法，使得在输入数据的相关矩阵含多重特征值时。

39个传统秋菊品种扦插生根能力综合评价

为了筛选出扦插生根能力较强的传统秋菊(Chrysanthemum morifolium Ramat.)品种,对39个传统秋菊品种扦插12 d的8个根系形态指标进行差异分析、相关性分析和主成分分析,并对这些传统秋菊品种的扦插生根能力进行隶属函数分析和分级。结果显示:供试传统秋菊品种的总根数为3.3~44.0,总根长、平均根长和最长根长分别为2.463~129.174、0.632~4.470和1.610~7.964 cm,根直径为0.200~0.405 mm,根投影面积和根表面积分别为0.102~3.062和0.319~9.619 cm 2,总根体积为0.002~0.079 cm 3,且多数指标以‘火凤凰’(‘Huofenghuang’)最小。这些根系形态指标的变异系数为15.789%~51.605%,其中,总根长的变异系数最大,根直径的变异系数最小,其余指标的变异系数均大于32%。供试传统秋菊品种的多数根系形态指标间存在显著(P<0.05)或极显著(P<0.01)正相关。主成分分析结果显示:前3个主成分的累计贡献率达97.976%。隶属函数分析和分级结果表明:供试传统秋菊品种的扦插生根能力分为优秀、良好、中等和偏差4个等级,‘龙都春丽’(‘Longdu Chunli’)、‘玉楼人醉’(‘Yulou Renzui’)、‘龙都月华’(‘Longdu Yuehua’)、‘圣光华宝’(‘Shengguang Huabao’)、‘龙都秋枫’(‘Longdu Qiufeng’)、‘龙都春晓’(‘Longdu Chunxiao’)的扦插生根能力优秀,综合得分均大于0.70。根投影面积、根表面积和总根体积在不同等级间差异显著。研究结果显示:不同传统秋菊品种间扦插苗的根系形态指标变异较大,其中,‘龙都春丽’等6个品种的扦插生根能力较强,可作为传统秋菊品种遗传改良的亲本。

MCA框架下Shearlet和DCT字典组合地震数据重建

相比于单一变换,形态分量分析(MCA)是信号稀疏表示的一种更有效的手段。在MCA框架下,提出了一种Shearlet字典和DCT字典组合的地震数据重建方法。首先,基于MCA框架,分别选取DCT字典和Shearlet字典稀疏表示地震数据中的局部奇异分量与平滑线状分量;随后,通过加入指数阈值模型和指数阈值函数的块坐标松弛(BCR)算法重建各个分量;最后,合并各个分量得到重建结果。合成数据实验和真实数据实验均表明,该方法能够有效地重建缺失地震数据,且重建精度高于单一Shearlet字典、Curvelet+DCT字典组合和Shearlet+Curvelet字典组合。

基于MCA的压缩感知彩色图像超分辨率重建

为了在无训练集的情况下,改善单幅彩色图像的超分辨率重建中容易出现的色彩失真、边缘模糊和混叠等问题,实现了一种基于形态成分分析的压缩感知彩色图像超分辨率重建算法.该算法先将彩色图像转换到YCbCr色彩空间,再将各色彩分量用形态成分分析方法分解为边缘、纹理和平滑成分,然后对各成分进行压缩感知超分辨率重建,且将重建结果合成为高分辨率的色彩分量,进一步线性融合即得高分辨率彩色图像.仿真实验表明,与传统插值算法和其他形态成分分析相关算法相比,客观评价指标和主观视觉效果得到显著提高.

基于MCA的乳腺X线图像中肿块的自适应检测方法

针对肿块通常大小和形状各异,并且边缘模糊的特点,提出了一种基于形态学成分分析(MCA)和直方图自适应阈值搜索的肿块检测方法.首先通过引入MCA方法有效地抑制了血管和纤维对检测的影响,在此基础上设计了一种基于直方图的自适应阈值搜索策略,根据肿瘤的生长特性,通过自适应阈值和多灰度同心层方法,有效地检测乳腺X线图像中的病变区域.通过对真实乳腺X线图像的测试实验,其结果表明,所提出的方法能够检测出不同类型的肿块区域,并且假阳性区域的数量在可接受的范围内,能够有效地辅助医生进行诊断.

基于超声辅助的汽车微小零部件内部缺陷无损检测方法

为了更准确、全面地分析汽车微小零部件缺陷,基于超声辅助的方式,设计了内部缺陷无损检测方法。根据超声波传播反射情况及声场变化,采集微小零部件图像;通过融合边缘信息消除图像中噪声,分割图像主体并完成边缘识别,确定存在内部缺陷的区域;利用核主成分分析技术,通过降维处理方式确定缺陷区域特征,进而完成无损检测。结果表明:利用所提方法获取的零部件图像清晰,缺陷检测准确率始终高于95%,对气泡和气孔缺陷的检测时间小于6 s,对裂纹缺陷的检测时间小于8 s,说明所提方法对内部缺陷的定位准确度和识别效率均较高。

一种基于细节特征的MCA多聚焦图像融合方法

针对多聚焦图像的特点,提出一种形态分量分析分解下的基于细节特征的图像融合方法。该方法首先将原图像分解成光滑部分和纹理部分。其次,为了充分体现多聚焦的纹理特征,在光滑部分采用微分算子定义的细节特征作为融合规则。最后,在纹理部分采用选择绝对值作为纹理部分的活跃程度的度量,使用绝对值最大法作为其融合规则。实验结果表明,该算法得到的融合图像优于其他多聚焦融合的算法。

结合MCA与Retinex算法的低照度图像增强方法

针对低照度条件下获取的图像存在可见光照度低、噪声大等问题,提出了一种基于形态成分分析(MCA)和Retinex算法结合的低照度图像增强方法。首先,将低照度图像转换到HSV色彩空间,接着采用MCA将V分量分解为纹理和平滑部分;其次对平滑部分采用基于改进的多尺度Retinex算法和自适应全局色调映射进行增强,对纹理部分进行维纳滤波去噪后再进行Laplace算子锐化;然后MCA重建得到亮度增强图像,将其与H、S分量合并且转换到RGB色彩空间;最后采用自适应函数恢复色彩得到最终增强图像。实验结果表明,该算法能够有效改善低照度图像质量,提高图像亮度,更好地保留边缘、细节纹理和抑制噪声。

基于LRMO及MCA的机载雷达风电场杂波抑制方法

风力发电迅速发展,风电场杂波使机载雷达产生大量虚假目标,导致机载雷达出现检测概率下降、虚警概率上升等问题。因此,研究机载雷达风电场杂波抑制方法对于提升机载雷达工作性能具有十分重要的意义。考虑到机载雷达风电场杂波先验信息无法实时获取、难以估计且机载雷达回波频谱更加复杂等特殊问题,本文基于低秩矩阵优化(LRMO)算法根据风电场杂波与目标微动特征随时间的不同变化特性,实现目标与风电场杂波处于不同距离单元的风电场杂波抑制。目标与风电场杂波处于同一距离单元时,考虑到LRMO算法存在的局限性,依据风电场杂波与目标的不同稀疏特性,利用形态成分分析(MCA)算法进行补充抑制风电场杂波。实验结果验证了所提方法的有效性。

MCA分解的唐墓室壁画修复算法

壁画数字化修复工作极大降低了手工修复时带来的不可逆的风险。根据唐墓室壁画人工修复时先整体结构、后局部纹理的思路,提出一种基于形态学成分分析(morphological component analysis,MCA)分解的唐墓室壁画修复算法。首先结合唐墓室壁画的特点,采用改进的MCA方法进行图像分解,得到结构部分和纹理部分;然后根据图像分解后纹理和结构的复杂程度与稀疏程度,分别采用简化的全变分(total variation,TV)算法和K奇异值分解(K-singular value decomposition,K-SVD)算法进行修复。实验结果表明,该算法可兼顾纹理与结构的修复效果,唐墓室壁画中的裂缝现象的破损修复精度得到提高。

基于压缩感知的加权MCA地震数据重构方法

地震数据规则化重构是地震资料处理十分重要的基础性工作.压缩感知理论打破了香农采样定理的制约,利用信号在某个变换域的稀疏特性重构出完整的信号,在地震数据重构领域得到了很好的应用.深反射地震剖面大都布置在地质构造比较复杂的区段,复杂的地质构造使深反射地震剖面上的波阻特征复杂,采用单一稀疏变换不能最有效地表征数据的内部结构特征.MCA(形态成分分析)方法将信号分解为几种形态特征区别明显的分量来逼近数据的内部复杂结构,但是对各成分简单的叠加仍然无法有效地描述复杂构造数据的各种特征.结合两种方法的优点,本文提出了一种新的基于压缩感知的重构算法框架,在MCA方法的基础上对各稀疏字典进行加权,在迭代中不断更新各个稀疏字典的权值系数,对信号内部的各种特征进行最优描述,从而实现对信号的高质量重构.模型测试和实际资料处理结果表明:基于压缩感知的加权MCA方法不仅可以对地质构造复杂的地震数据进行高效的插值重建,而且可以很好的消除空间假频.