改进的小波域自适应阈值煤尘图像去噪

煤尘图像在采集和传输过程中受到了噪声的污染。基于Bayes shrinke的小波域自适应阈值去噪算法取得了较好的图像去噪效果,但其阈值的参数估计是在假定噪声和信号不相关情况下得到的,使得其去噪效果降低。提出了一种改进的小波域自适应阈值去噪算法,从而改进了广义高斯分布模型参数的估计方法,并在增加计算量不大的情况下提高了参数估计精度。用改进的小波域自适应阈值去噪算法对煤尘图像进行去噪处理。仿真结果表明,新算法提高了煤尘图像的去噪效果,并且计算量较小,能够满足对煤尘浓度实时测量的要求。     

基于混合线性模型的图像去噪

现有的图像去噪算法大多假设图像为分段平滑信号,通过滤除图像中的振动分量达到去噪的效果.如果将这类方法应用于纹理图像,则会导致图像细节信息的损失.该文针对保留图像细节的问题,提出了基于混合线性模型的去噪方法.新方法不假设图像分段平滑,仅假设图像具有自相似性,利用图像的相似性区分图像信号与噪声.文中使用统计学习的方法对图像区域进行聚类,并得到图像主成份,以主成份作为信号分量重组图像,从而对纹理图像取得很好的去噪效果.     

基于分数阶Riemann-Liouville积分的图像去噪

为了在获得更好去噪性能的同时更多地保留图像纹理信息,介绍了分数阶Riemann-Liouville(R-L)积分算子在信号滤波中的作用,将分数阶R-L积分理论引入到数字图像去噪中,并利用阶梯逼近方法来实现数值计算。模型通过设定微小的积分阶次来构建相应的图像去噪掩模,由此实现噪声图像的局部微调,并利用迭代的思想来控制模型的去噪强度,从而获得较好的图像去噪效果。实验结果表明,基于分数阶R-L积分的图像去噪算法较传统的去噪方法不仅可以提高图像的信噪比(SNR),所提出的算法去噪后图像的信噪比为18.3497dB,较传统去噪方法最低也提升了大约4%,而且可以更好地保留图像的弱边缘和纹理等细节信息。     

独立分量分析在有噪图像分离中的应用

独立分量分析(independent component analysis，ICA)是基于信号高阶统计量的盲源分离方法。在分析独立分量分析的基本模型及方法的基础上，讨论了有噪信号的独立分量分析(Noisy ICA)，利用小波阈值去噪和FastICA算法进行了有噪混合图像分离的仿真研究。结果表明，对于含有加性观测噪声的混合图像的分离，先去噪处理再进行独立分量分离的效果要优于独立分量分离后再去噪的效果。     

一种基于M带小波变换的多幅图像去噪算法

基于小波的图像去噪算法是目前图像去噪研究的一个热点。大多数的研究考虑的都是单幅图像样本的情况,在基于图像的多幅含不同均匀噪声拷贝的两带小波去噪方法的基础上,把多幅含噪图像拷贝的两带小波去噪方法推广到了M带,提出了一种基于M带小波变换的多幅图像去噪方法。实验结果表明该方法的去噪效果要优于相同条件下的两带小波的去噪效果。     

异型纤维图像去噪分析

为了去除异型纤维图像中的噪声,首先分析了异型纤维图像中的噪声模型,然后针对噪声模型提出了一种能同时去除异型纤维图像中高斯和脉冲混合噪声的去噪算法.该算法在全变差(Total Variation,TV)算法的基础上进行了算法改进,综合了中值滤波的优点,在达到去噪目的的同时,较好地处理了去除噪声、保留边缘细节信息这对在图像去噪中存在的矛盾.同时,对参数的选取也做了分析,较好地平衡了去噪效果和处理效率问题.数值对比实验中的视觉效果和客观标准均表明了该去噪算法的有效性。     

基于自监督约束的双尺度真实图像盲去噪算法

现存的图像去噪算法在处理加性高斯白噪声上已经取得令人满意的效果,然而其在未知噪声强度的真实噪声图像上泛化性能较差.鉴于深度卷积神经网络极大地促进了图像盲去噪技术的发展,针对真实噪声图像提出一种基于自监督约束的双尺度真实图像盲去噪算法.首先,所提算法借助小尺度网络分支得到的初步去噪结果为大尺度分支的图像去噪提供额外的有用信息,以帮助后者实现良好的去噪效果.其次,用于去噪的网络模型由噪声估计子网络和图像非盲去噪子网络构成,其中噪声估计子网络用于预测输入图像的噪声强度,非盲去噪子网络则在所预测的噪声强度指导下进行图像去噪.鉴于真实噪声图像通常缺少对应的清晰图像作为标签,提出了一种基于全变分先验的边缘保持自监督约束和一个基于图像背景一致性的背景自监督约束,前者可通过调节平滑参数将网络泛化到不同的真实噪声数据集上并取得良好的无监督去噪效果,后者则可借助多尺度高斯模糊图像之间的差异信息辅助双尺度网络完成去噪.此外,还提出一种新颖的结构相似性注意力机制,用于引导网络关注图像中微小的结构细节,以便复原出纹理细节更加清晰的真实去噪图像.相关实验结果表明在SIDD,DND和Nam这3个真实基准数据集上,所提的基于自监督的双尺度盲去噪算法无论在视觉效果上还是在量化指标上均优于多种有监督图像去噪方法,且泛化性能也得到了较为明显的提升.     

基于稀疏表达的图像文档二值化技术仿真研究

研究了针对文档图像或者图形图像的去噪算法.传统二值化文档图像去噪算法无法满足识别准确性的需要的缺陷,利用文档图像或者图形图像中的线段方向信息,提出了一种利用曲线波变换和基追踪去噪算法相结合的图像去噪算法.首先进行曲线波变化,从而得到一个噪声图像的稀疏表达,然后利用基追踪去噪算法得到一个对无噪声图像的最优估计.在最优化计算过程中引入了对图像噪声的估计,从而能获得更好的图形去噪效果.实验显示改进的方法能较传统方法能够提供更好的去噪结果.能为图形处理提供良好的输入图像,可被图形识别和处理系统广泛应用.     

基于curvelet变换和独立分量分析的含噪盲源分离

独立分量分析（ICA）是基于信号高阶统计量的盲源分离方法,在高阶统计量方法中,由于高斯信号的高阶累计量为零,所以系统存在加性高斯噪声时就难以处理。提出了一种基于curvelet阈值去噪和FastICA算法的含噪信号盲分离的方法,并对高斯噪声环境下的混合图像进行了盲分离的仿真。结果表明,该方法能很好地解决由于存在加性高斯噪声而导致经典ICA算法性能发生严重恶化的问题;同时将curvelet变换去噪应用于含噪图像的盲源分离中,可以提高混合图像的信噪比,相对于小波去噪后的ICA算法,其分离性能有很大改善。     

一种2D-VMD与NLM结合的芯片图像去噪算法研究

为了提高智能电表芯片图像的字符识别精度,需要消除芯片图像中的噪声,以减小干扰;文章提出了一种基于二维变分模态分解算法(2D-VMD)与非局部均值(NLM)滤波的芯片图像去噪算法;首先利用2D-VMD将含有噪声信号的芯片图像分解为K个模态分量;然后根据提出的结构相似(SSIM)阈值设置方法确定噪声分量并将其去除,使用剩余的有效分量重构图像;最后通过非局部均值滤波算法对重构后的图像进行处理,进一步滤除残余噪声,达到二次去噪的效果;实验结果表明,相比传统的图像去噪算法,提出的算法能在较好保留原始芯片图像的字符信息的基础上,去除不相关的噪声干扰,使去噪后的芯片图像的均方误差值变小,峰值信噪比增大,提高芯片图像质量.     

Blind separation of non-stationary sources using continuous density hidden Markov models

Blind source separation (BSS) has attained much attention in signal processing society due to its ‘blind’ property and wide applications. However, there are still some open problems, such as underdetermined BSS, noise BSS. In this paper, we propose a Bayesian approach to improve the separation performance of instantaneous mixtures with non-stationary sources by taking into account the internal organization of the non-stationary sources. Gaussian mixture model (GMM) is used to model the distribution of source signals and the continuous density hidden Markov model (CDHMM) is derived to track the non-stationarity inside the source signals. Source signals can switch between several states such that the separation performance can be significantly improved. An expectation-maximization (EM) algorithm is derived to estimate the mixing coefficients, the CDHMM parameters and the noise covariance. The source signals are recovered via maximum a posteriori (MAP) approach. To ensure the convergence of the proposed algorithm, the proper prior densities, conjugate prior densities, are assigned to estimation coefficients for incorporating the prior information. The initialization scheme for the estimates is also discussed. Systematic simulations are used to illustrate the performance of the proposed algorithm. Simulation results show that the proposed algorithm has more robust separation performance in terms of similarity score in noise environments in comparison with the classical BSS algorithms in determined mixture case. Additionally, since the mixing matrix and the sources are estimated jointly, the proposed EM algorithm also works well in underdetermined case. Furthermore, the proposed algorithm converges quickly with proper initialization.     

Blind Source Separation Using Quadratic form Innovation

Blind source separation (BSS) is an increasingly popular data analysis technique with many applications. Several methods for BSS using the statistical properties of original sources have been proposed, for a famous one, such as non-Gaussianity, which leads to independent component analysis (ICA). This paper proposes a blind source separation method based on a novel statistical property: the quadratic form innovation of original sources, which includes linear predictability and energy (square) predictability as special cases. A gradient learning algorithm is presented by minimizing a loss function of the quadratic form innovation. Also, we give the stability analysis of the proposed BSS algorithm. Simulations verify the efficient implementation of the proposed method.     

Towards an intelligent multi-sensor satellite image analysis based on blind source separation using multi-source image fusion

In this paper we propose a new approach for land cover classification using blind sources separation (BSS) and satellite image fusion methods simultaneously. Satellite image pixels are represented by radiometric values where each pixel is considered as a mixture of several independent sources. The BSS methods were developed in order to extract maximum information from different statistical features such as spatial correlation and local high order statistics. The statistical independence of the sources can be obtained through the joint approximate diagonalization of eigen-matrix in two dimensions (JADE-2D) algorithm. A reduction of spatial correlation can be obtained using the second order blind identification in two dimensions (SOBI-2D) algorithm. Non-Gaussianity can be measured using the fast-independent component analysis in two dimensions (Fast-ICA-2D) algorithm. These algorithms allow extraction of features by estimating the source images, mixing and un-mixing the matrix. These source images will be used by our framework as secondary knowledge, which is useful for a supervised classification.     

基于Hough变换的时频盲源分离算法

1998年,Belouchrani,A和Amin,M.G基于时频分布提出了一种经典的时频盲源算法,不足是当有噪声存在时,性能会下降。主要考虑源噪声的盲源分离问题,以Wigner分布计算观测信号的时频阵并将其看做图像,利用Hough变换将信号检测转换为在参数空间寻找局部极大值的问题,运用自项点理论选择合适的矩阵进行联合近似对角化估计源信号。该方法扩展了盲源分离的限制条件,且通过把噪声能量扩展到整个参数平面而只选择信号能量占主导的时频点,对噪声具有一定的抑制能力。     

基于独立分量分析和遗传算法的图象分离方法研究与实现

在深入分析独立分量分析技术的基础上，针对常规数值求解方法容易陷入局部最优解的问题，提出了一种基于遗传算法和独立分量分析相结合的盲源分离新算法．通过对图象信号分离仿真试验表明，采用最佳保留机制和移民方式的动态补充子代个体操作，在一定的群体规模和遗传代数的情况下，该方法能实现信号的盲分离，并可获得全局最优解．对超高斯信号和亚高斯信号的混合信号，与扩展信息最大化方法相比，该方法可获得更好的分离效果。     

Blind source separation with dynamic source number using adaptive neural algorithm

A difficult blind source separation (BSS) issue dealing with an unknown and dynamic number of sources is tackled in this study. In the past, the majority of BSS algorithms familiarize themselves with situations where the numbers of sources are given, because the settings for the dimensions of the algorithm are dependent on this information. However, such an assumption could not be held in many advanced applications. Thus, this paper proposes the adaptive neural algorithm (ANA) which designs and associates several auto-adjust mechanisms to challenge these advanced BSS problems. The first implementation is the on-line estimator of source numbers improved from the cross-validation technique. The second is the adaptive structure neural network that combines feed-forward architecture and the self-organized criterion. The last is the learning rate adjustment in order to enhance efficiency of learning. The validity and performance of the proposed algorithm are demonstrated by computer simulations, and are compared to algorithms with state of the art. From the simulation results, these have been confirmed that the proposed ANA performed better separation than others in static BSS cases and is feasible for dynamic BSS cases.     

Fast nonlinear autocorrelation algorithm for source separation

Independent component analysis (ICA) and blind source separation (BSS) methods have been used for pattern recognition problems. It is well known that ICA and BSS depend on the statistical properties of original sources or components, such as non-Gaussianity. In the paper, using a statistical property—nonlinear autocorrelation and maximizing the nonlinear autocorrelation of source signals, we propose a fast fixed-point algorithm for BSS. We study its convergence property and show that its convergence speed is at least quadratic. Simulations by the artificial signals and the real-world applications verify the efficient implementation of the proposed method.     

基于小波域的图像噪声估计新方法

提出一种基于小波域区域分割的估计图像噪声的新疗法。该方法利用图像的小波高频系数，在提出图像平滑区域的基础上，准确地估计图像高斯噪声的标准方差、由于考虑了图像的局部信息，因此该方法优于传统的估计方法。用于多幅实验图像的结果表明：在图像受噪声比较小或图像含高频信息较丰富时，该方法比传统疗法更准确。     

Image splicing localization using PCA-based noise level estimation

Image splicing is one of the most common image tampering operations, where the content of the tampered image usually significantly differs from that of the original one. As a consequence, forensic methods aiming to locate the spliced areas are of great realistic significance. Among these methods, the noise based ones, which utilize the fact that images from different sources tend to have various noise levels, have drawn much attention due to their convenience to implement and the relaxation of some operation specific assumptions. However, the performances of the existing noise based image splicing localization methods are unsatisfactory when the noise difference between the original and spliced regions is relatively small. In this paper, through incorporation of a recent developed noise level estimation algorithm, we propose an effective image splicing localization method. The proposed method performs blockwise noise level estimation of a test image with principal component analysis (PCA)-based algorithm, and segments the tampered region from the original region by k-means clustering. The experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods, especially for practical image splicing, where the noise difference between the original and spliced regions is typically small.     

盲信号分离和序贯滤波的SAR影像水体提取

采用一种新的基于盲信号分离（BSS）和序列非线性滤波方法实现多极化合成孔径雷达（SAR）影像相干斑噪声抑制和水体目标快速提取。SAR影像具有强烈乘性相干斑噪声,影像数据为非高斯分布,但其具体分布形式及参数难以获得。利用基于独立分量分析的盲信号分离方法,不需要知道SAR影像的具体分布,通过对数量化将相干斑噪声转化为与图像数据相互独立的加性噪声,从多极化SAR影像中自动分离出图像数据与相干斑噪声,并自动选择相干斑指数最小的分量为图像分量。针对SAR影像水体目标的亮度及形状分布特征,进一步采用序列非线性滤波处理,从分离出的图像分量中提取出水体目标。利用ENVISAT ASAR多极化影像进行了实验,结果表明该方法可以快速准确地提取多极化SAR影像中的水体目标。