基于独立分量分析的红外线列扫描图像处理方法

独立分量分析(Independent component analysis,ICA)作为一种有效的盲源分离方法,其目的是从由传感器收集到的混合信号中分离出相互独立的源信号,使得这些分离出来的信号之间尽可能的相互独立。针对红外线列扫描图像,提出了一种基于ICA的图像增强方法,该方法能够有效地去除红外线列扫描图像的非均匀性干扰。阐述了ICA的基本原理,介绍了基于负熵判据的FastICA算法,给出了该方法的具体实现步骤及相应的实验处理结果。结果表明,利用该方法能够达到图像增强的目的。     

An independent component analysis-based approach on ballistocardiogram artifact removing

Interest about simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data acquisition has rapidly increased during the last years because of the possibility that the combined method offers to join temporal and spatial resolution, providing in this way a powerful tool to investigate spontaneous and evoked brain activities. However, several intrinsic features of MRI scanning become sources of artifacts on EEG data. Noise sources of a highly predictable nature such as those related to the pulse MRI sequence and those determined by magnetic gradient switching during scanning do not represent a major problem and can be easily removed. On the contrary, the ballistocardiogram (BCG) artifact, a large signal visible on all EEG traces and related to cardiac activity inside the magnetic field, is determined by sources that are not fully stereotyped and causing important limitations in the use of artifact-removing strategies. Recently, it has been proposed to use independent component analysis (ICA) to remove BCG artifact from EEG signals. ICA is a statistical algorithm that allows blind separation of statistically independent sources when the only available information is represented by their linear combination. An important drawback with most ICA algorithms is that they exhibit a stochastic behavior: each run yields slightly different results such that the reliability of the estimated sources is difficult to assess. In this preliminary report, we present a method based on running the FastICA algorithm many times with slightly different initial conditions. Clustering structure in the signal space of the obtained components provides us with a new way to assess the reliability of the estimated sources.     

宽带电磁图像卷积神经网络盲恢复方法研究

在利用抛物反射面对电磁干扰源成像过程中,由于系统衍射受限及成像频带较宽,导致干扰源成像模糊,分辨率低,难以分辨,不同频率不同区域干扰源所成图像分辨率不同,采用已有超分辨算法难以提高分辨率。为了实现宽带电磁图像的盲复原, 应用卷积神经网络的方法。网络训练是直接输入模糊图像,不假设任何特定的模糊和噪声模型情况下,重建出高质量图像。实验和仿真结果证明了卷积神经网络盲恢复方法在宽频带不同成像区域下表现了优于其他盲恢复算法的优势。     

一种电磁干扰源成像多分辨率分区算法

在利用抛物反射面对电磁干扰源成像过程中,由于系统衍射受限导致干扰源成像模糊,分辨率低,难以分辨,由于不同频率不同区域干扰源所成图像分辨率不同,具有分区域多分辨率的特征,采用已有超分辨算法难以提高分辨率。利用Mean Shift算法,在原有算法基础上改进使其能够适应多分辨率的电磁干扰源成像,在图像分割的基础上对多分辨率图像进行分块抽离,并采用基于L_R迭代的盲反卷积算法分别对各区域进行分辨率的提高,仿真结果表明算法能够适应对干扰源的多分辨率电磁成像并提高分辨率。     

Single frame blind image deconvolution by non-negative sparse matrix factorization

Novel approach to single frame multichannel blind image deconvolution has been formulated recently as non-negative matrix factorization problem with sparseness constraints imposed on the unknown mixing vector that accounts for the case of non-sparse source image. Unlike most of the blind image deconvolution algorithms, the novel approach assumed no a priori knowledge about the blurring kernel and original image. Our contributions in this paper are: (i) we have formulated generalized non-negative matrix factorization approach to blind image deconvolution with sparseness constraints imposed on either unknown mixing vector or unknown source image; (ii) the criteria are established to distinguish whether unknown source image was sparse or not as well as to estimate appropriate sparseness constraint from degraded image itself, thus making the proposed approach completely unsupervised; (iii) an extensive experimental performance evaluation of the non-negative matrix factorization algorithm is presented on the images degraded by the blur caused by the photon sieve, out-of-focus blur with sparse and non-sparse images and blur caused by atmospheric turbulence. The algorithm is compared with the state-of-the-art single frame blind image deconvolution algorithms such as blind Richardson-Lucy algorithm and single frame multichannel independent component analysis based algorithm and non-blind image restoration algorithms such as multiplicative algebraic restoration technique and Van-Cittert algorithms. It has been experimentally demonstrated that proposed algorithm outperforms mentioned non-blind and blind image deconvolution methods.     

基于ICA的X射线医学图像目标提取

X射线医学成像能观察到患者体内病变组织,对医学诊断有重要参考价值。针对传统医学X射线图像噪声强、层次感差和器官组织重叠的问题,提出利用多能谱X射线成像结合独立成分分析(independent component analysis, ICA)进行图像去噪和目标提取。首先ICA结合稀疏编码收缩法对图像降噪预处理以保证目标提取精度;然后根据图像中各目标组成特性,分离图像中每个像素对应的目标厚度矩阵;最后ICA以盲分离理论获得收敛矩阵重建出目标对象。在ICA算法中,借助于主观评价标准,发现当收敛次数大于40时目标分离成功;当幅值尺度在[25, 45]区间内,目标图像对比度高且失真较小。同时,通过观测实验得到的三维峰值信噪比图表明：ICA算法中收敛次数和幅值对图像质量有较大影响,当重建图像的对比度和边缘信息均达到较好效果时,收敛次数与幅值为85和35。     

Time-delay estimation of acoustic emission signals using ICA

Acoustic emission (AE) analysis is used for characterization and location of developing defects in materials. AE sources often generate a mixture of various statistically independent signals. One difficult problem of AE analysis is the separation and characterization of signal components when the signals from various sources and the way in which the signals were mixed are unknown. Recently, blind source separation by independent component analysis (ICA) has been used to solve these problems. The main purpose of this paper is to demonstrate the applicability of ICA to time-delay (T-D) estimation of two independent continuous AE sources on an aluminum beam. It is shown that it is possible to estimate T-Ds by ICA, and thus to locate two independent simultaneously emitted sources.     

Enhancements in full‐field PIXE imaging—Large area elemental mapping with increased lateral resolution devoid of optics artefacts

The combination of a pn‐junction charge‐coupled device‐based pixel detector with a poly‐capillary X‐ray optics was installed and examined at the Helmholtz‐Zentrum Dresden‐Rossendorf. The set‐up is intended for particle‐induced X‐ray emission imaging to survey the trace elemental composition of flat/polished geological samples. In the standard configuration, a straight X‐ray optics (20 μm capillary diameter) is used to guide the emitted photons from the sample towards the detector with nearly 70 000 pixels. Their dimensions of 48 × 48 μm² are the main limitation of the lateral resolution. This limitation can be bypassed by applying a dedicated subpixel algorithm to recalculate the footprint of the photon's electron cloud in the detector. The lateral resolution is then mainly determined by the capillary's diameter. Nevertheless, images are still superimposed by the X‐ray optics pattern. The optics' capillaries are grouped in hexagonal bundles resulting in a reduced transmission of X‐rays in the boundary regions. This influence can be largely suppressed by combining a series of short measurements at slightly shifted positions using a precision stage and correcting the image data for this shifting. The use of a subpixel grid for the image reconstruction allows a further increase of the spatial resolution. This approach of image‐stacking and multiframe super‐resolution in combination with the subpixel correction algorithm is presented and illustrated with experimental data. Additionally, a flat‐field correction is shown to remove the remaining imaging inhomogeneity caused by non‐uniform X‐ray transmission. The described techniques can be used for all X‐ray spectrometry methods using an X‐ray camera to obtain high‐quality elemental images.     

一种新的空谱联合探测高光谱影像目标探测算法

高光谱遥感影像不但具有高分辨率的空间信息还包含连续的光谱信息,因此在目标探测领域具有独特的应用优势。传统的高光谱遥感影像目标探测侧重于光谱信息的应用,形成了确定性算法和统计学算法。确定性算法通过计算目标光谱与待检测光谱之间的距离来查找目标,不能检测亚像素目标,而且容易受到噪声的影响;统计学目标检测计算背景统计特性,通过探测异常点来检测目标,可以检测亚像素目标和小目标,但容易受到目标尺寸的影响,不能很好的检测大目标。随着高光谱遥感影像的空间分辨率的增加,探测目标已有亚像素目标逐步转换为单像素及多像素目标,此时,在高光谱图像中,相同类别的地物在空间分布上呈现聚类特性, 因此,在利用高光谱遥感影像进行目标探测时,需要将其空间信息融入算法中。将空间特征引入传统目标探测算法。提出了一种新的空谱结合的高光谱目标探测算法,将传统的基于统计的目标探测算子与空域邻域聚类算法相结合,首先利用目标探测算子将影像划分为潜在目标区域与背景区域;通过计算潜在目标区域的质心,以质心为中心进行邻域聚类,剔除潜在目标区域中的背景区域,通过迭代计算获取最终目标探测结果。传统的基于统计的目标探测算子,将整个探测区域定义为背景区域,实现对背景区域的统计特征提取,而该方法将背景区域与潜在目标区域分离,剔除了目标区域对背景区域的统计干扰。将本算子与传统的约束能量最小化算子和自适应余弦探测算子进行分析比较可知,该算子的大目标探测性能优于传统的统计算子。     

Optimizing ICA in fMRI using information on spatial regularities of the sources

Spatial independent component analysis (ICA) is a well-established technique for multivariate analysis of functional magnetic resonance imaging (fMRI) data. It blindly extracts spatiotemporal patterns of neural activity from functional measurements by seeking for sources that are maximally independent. Additional information on one or more sources (e.g., spatial regularity) is often available; however, it is not considered while looking for independent components. In the present work, we propose a new ICA algorithm based on the optimization of an objective function that accounts for both independence and other information on the sources or on the mixing model in a very general fashion. In particular, we apply this approach to fMRI data analysis and illustrate, by means of simulations, how inclusion of a spatial regularity term helps to recover the sources more effectively than with conventional ICA. The improvement is especially evident in high noise situations. Furthermore we employ the same approach on data sets from a complex mental imagery experiment, showing that consistency and physiological plausibility of relatively weak components are improved.     

高光谱遥感图像光谱解混的独立成分分析技术

高光谱遥感在对地球陆地、海洋、大气的观测中发挥着重要作用,高光谱遥感图像分析的关键是提取像元光谱内部各物质成分及其含量,即光谱解混。独立成分分析提供了一种先进的技术手段,在很少先验知识的前提下,实现端元(物质成分)光谱及其丰度(含量)的同时提取。但丰度约束破坏了各成分独立的前提条件,导致了独立成分分析的局限性。针对这一问题,提出了丰度约束下总体相关性最小化的解决方案,并指出总体相关性最小化下的理想角度,通过设计角度修正的独立成分分析算法把各成分调整到理想角度上。利用模拟数据与真实数据算法进行检验,结果表明：经过角度修正后,独立成分分析突破了原有的局限性,有助于进一步提高独立成分分析技术在光谱分析中的有效性。     

基于非采样Contourlet变换的遥感图像融合算法

为了使融合后的多光谱图像在尽可能保持原始多光谱图像光谱特性的同时,显著提高空间分辨力,提出了一种基于非采样Contourlet变换(NSCT)的遥感图像融合算法。算法首先对全色波段图像进行非采样Contourlet变换,得到全色波段图像的低频子带系数和各带通方向子带系数;然后针对多光谱图像的每一个波段,将其进行双线性插值后作为融合后多光谱图像的低频子带系数,对全色波段图像的各带通方向子带系数采用基于成像系统物理特性的注入模型(调整系数)进行局部调整后,作为融合后多光谱图像的各带通方向子带系数,从而得到融合后多光谱图像的非采样Contourlet变换系数;最后再经非采样Contourlet逆变换得到该波段具有高空间分辨力的多光谱图像。采用IKONOS卫星遥感图像进行了仿真实验,实验结果表明,该算法在光谱保留和空间质量提高方面优于其它传统的遥感图像融合算法。     

基于B样条插值算法的亚像元技术的研究

亚像元动态成像技术是目前实现卫星相机小型化及提高相机空间分辨率的一种有效方法。在不改变TDICCD相机的焦距、成像距离,以及TDI CCD器件的像元尺寸的前提下,亚像元动态成像技术可以提高相机的空间分辨率。利用B样条插值算法,研究了TDI CCD相机的图像的亚像元动态插值问题,由两幅相机输出的原始图像经过算法得到比原图像分辨率高的图像,并对比了该算法与其它几种算法的效果,分析结果表明,该算法较其它算法得到的高分辨率图像的效果更佳。     

基于多方向多尺度变换的图像压缩算法

针对小波变换方向选择性差的局限,提出了一种多方向多尺度的的图像变换。圆对称滤波器组首先将图像分解为高频子带和低频子带,然后利用方向滤波器组将高频子带分解为多个方向子带,而对低频子带进行小波变换。多方向多尺度变换能以更稀疏的方式表示图像的边缘和纹理等几何特征,有利于图像压缩。在该变换基础上,结合迭代量化、嵌入式块截断编码(EBCOT)和集合分裂嵌入式块编码(SPECK)构建一种压缩算法。实验结果表明,对于纹理和边缘丰富的图像,压缩算法的性能相对于JPEG2000有明显地提高。     

Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain

Multifocus image fusion aims at overcoming imaging cameras's finite depth of field by combining information from multiple images with the same scene. For the fusion problem of the multifocus image of the same scene, a novel algorithm is proposed based on multiscale products of the lifting stationary wavelet transform (LSWT) and the improved pulse coupled neural network (PCNN), where the linking strength of each neuron can be chosen adaptively. In order to select the coefficients of the fused image properly with the source multifocus images in a noisy environment, the selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed, respectively. For choosing the low frequency subband coefficients, a new sum modified-Laplacian (NSML) of the low frequency subband, which can effectively represent the salient features and sharp boundaries of the image in the LSWT domain, is an input to motivate the PCNN neurons; when choosing the high frequency subband coefficients, a novel local neighborhood sum of Laplacian of multiscale products is developed and taken as one type of feature of high frequency to motivate the PCNN neurons. The coefficients in the LSWT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT-PCNN-based image fusion methods even though the source image is in a noisy environment in terms of both visual quality and objective evaluation.     

Blind deconvolution: multiplicative iterative algorithm

A new algorithm has been developed for performing blind deconvolution on degraded images. The algorithm naturally preserves the nonnegative constraint on the iterative solutions of blind deconvolution and can produce a restored image of high resolution. Furthermore, benefiting from the multiplicative form, the algorithm is free from the instability of numerical computation. Results of applying the algorithm to simulated and real degraded images are reported.     

Subpixel image registration with reduced bias

A backward linear digital image correlation algorithm was introduced to obtain subpixel image registration without noise-induced bias for an image set consisting of a noise-free reference image and a number of noisy current images. Furthermore, a correction procedure using additional reference images (generated by offsetting the original image to displacement increments of either half-pixels or even quarter-pixels) was proposed to reduce subpixel approximation bias in the analysis. Numerical results of six sets of synthetic images showed that the proposed algorithm was effective in improving the accuracy of subpixel image registration.     

基于标准互相关函数的图像分辨率自动标定方法

理想规则图像特征经标准互相关函数匹配后,相似函数C*(x)可用确定的解析式Z(x)表达.但是对于含噪声图像,相似函数C(x)较之C*(x)发生了变化,但存在"零相似不变性".将此原理应与于显微视觉中图像物面分辨率的在线标定,推导了矩形图像特征的一维相似函数解析式Z(x);求解C(x)=0作为Z(x)的近似,并给出了具体标定算法;通过仿真图像实验,给出标定算法的正确度在0.1～0.2 pixel;最后,将标定算法应用于可连续变焦的微对准装配系统,实测算法的精密度可达为0.08 pixel.实验结果表明,基于"零相似不变性"的标定算法是具有实用性的亚像素标定方法.～290nm波长激发下,310～390nm范围内产生荧光光谱,峰位波长在350nm,最佳激励波长为250nm;采用垂直偏振片(起偏角为0°)起偏照射样品,发现偏振荧光峰位不变,荧光峰强度随检偏角的增大而呈明显的线性递减关系.由实验数据计算得到荧光偏振度为0.783,表明分子具有一定确定取向;另外,分析认为酸性橙Ⅱ产生荧光,是由于分子中含有苯环和萘环结构吸收紫外光能量,以及氮键在光子作用下形成顺式异构体的激发单线态后,两者发射的光子所致.整个结果对酸性橙Ⅱ在食品中的违禁使用检测、特性表征、以及分子规律的更深入研究,有一定的参考价值.     

非下采样变换的红外与可见光图像融合

基于非下采样Contourlet变换（NSCT）,提出了一种红外和可见光图像融合算法。针对低频子带系数和各带通方向子带系数分别提出了基于图像物理特征的系数加权选择方式与基于区域能量匹配的系数选择方式,即低频基于区域梯度信息、高频基于区域特征因子的加权与选择结合的图像融合算法。实验结果表明：非下采样Contourlet变换具有较快的运算速度,且经非下采样变换后能量更加集中,可提供更多的图像信息。相对于基于像素的图像融合算法,本文的图像融合算法具有更高的融合性能,是一种更适合图像融合的多尺度几何分析（MGA）工具。     

复Contourlet域有向图与高斯混合模型的声呐图像增强

提出了复Contourlet域(CCT)中有向图与高斯混合模型的声呐图像增强算法。采用复Contourlet分析提取各尺度中声呐图像每一方向的弱特征信息;为建立特征信息间的联系,考虑复Contourlet域相邻尺度间子带系数的状态具有Markov性,子节点系数的状态依赖于父节点系数状态,构建有向概率图模型反映复系数的这种持续性;尺度内,构建高斯混合模型来建立同尺度中特性信息的联系,以两状态高斯混合模型来表征子带系数的非高斯边缘分布;最后,采用期望最大(EM)算法训练模型参数估计增强图像的系数,实现声呐图像增强。实验结果表明,本文算法与小波域隐马尔可夫树(HMT)算法、Contourlet域HMT算法相比,峰值信噪比(PSNR)增大4 dB以上,结构相似(SSIM)指数增加0.3;本文算法不仅能较好地抑制了声呐图像的强噪声,同时保留了图像边缘和轮廓等弱特征信息。