混沌信号自适应协同滤波去噪

混沌信号协同滤波去噪算法充分利用了混沌信号的自相似结构特征,具有良好的信噪比提升性能.针对该算法的滤波参数优化问题,考虑到最优滤波参数的选取受到信号特征、采样频率和噪声水平的影响,为提高该算法的自适应性使其更符合实际应用需求,基于排列熵提出一种滤波参数自动优化准则.依据不同噪声水平的混沌信号排列熵的不同,首先选取不同滤波参数对含噪混沌信号进行去噪,然后计算各滤波参数对应重构信号的排列熵,最后通过比较各重构信号的排列熵,选取排列熵最小的重构信号对应的滤波参数为最优滤波参数,实现滤波参数的优化.分析了不同信号特征、采样频率和噪声水平情况下滤波参数的选取规律.仿真结果表明,该参数优化准则能在不同条件下对滤波参数进行有效的自动最优化,提高了混沌信号协同滤波去噪算法的自适应性.     

A novel denoising method based on Radon transform and filtered back-projection reconstruction algorithm

A novel denoising method based on Radon transform and filtered back-projection (FBP) image reconstruction algorithm was proposed. This method can be considered as a special mean filter on projection line, which is different from most of the traditional filters operated on adjacent templates that could bring serious blurs to images. The details of images processed by the proposed method can be preserved relatively complete and the denoising effect is satisfactory. To verify the denoising effect of the proposed method, the simulation was designed and carried out, and the image evaluation parameters were applied to analyze the denoising effect and the detail-preserving ability quantitatively. For further understanding of the proposed method, the basic denoising principle of this method was analyzed. Noise points and information points can be distinguished: the attenuation velocity of gray scale of noise points is faster than that of information points, which was verified by the experiment. The results of different parameters in the proposed method were compared and analyzed. Several kinds of traditional filters were compared with the proposed method, and the result shows that the proposed method is better than the traditional filters in the aspects of both denoising effect and detail-preserving ability. Apart from this, the proposed method is not particular about the kind of noise; therefore, it is a powerful method to deal with atypical noise, uncertain noise, and mixed noises.     

多算法融合的自适应图像增强方法

提出一种多算法融合的图像增强方法,用于工程应用中的复杂降质图像的细节特征恢复.该方法汲取了Laplacian变换法、Sobel梯度法、盒状滤波法、非锐化掩蔽法及灰度幂律法等算法的优点,可对模糊图像进行自适应增强.通过拉普拉斯滤波器和梯度滤波器将原始图像分为基础层、细节层及边缘特征层;对微小细节信息及边缘特征信息进行增强,对基础信息进行压缩;然后采用盒装滤波器对图像的三个分层进行平滑过度及噪音过滤,最后使用非锐化掩蔽法和灰度变换来增加图像灰度的动态范围,从而得到增强后的图像.在相同的工况下,该方法分别与直方图均衡法、自适应伽马矫正法及小波变换的图像增强法实验结果进行对比,结果表明,该方法将图像的清晰度提高了13.1%~126.1%,能有效地处理复杂型感染的图像,避免图像过度增强,可以获得适合人眼的最佳视觉细节内容的增强效果.     

Image enhancement via Median-Mean Based Sub-Image-Clipped Histogram Equalization

This paper presents a robust contrast enhancement algorithm based on histogram equalization methods named Median-Mean Based Sub-Image-Clipped Histogram Equalization (MMSICHE). The proposed algorithm undergoes three steps: (i) The Median and Mean brightness values of the image are calculated. (ii) The histogram is clipped using a plateau limit set as the median of the occupied intensity. (iii) The clipped histogram is first bisected based on median intensity then further divided into four sub images based on individual mean intensity, subsequently performing histogram equalization for each sub image. This method achieves multi objective of preserving brightness as well as image information content (entropy) along with control over enhancement rate, which in turn suits for consumer electronics applications. This method avoids excessive enhancement and produces images with natural enhancement. The simulation results show that MMSICHE method outperforms other HE methods in terms of various image quality measures, i.e. average luminance, average information content (entropy), absolute mean brightness error (AMBE) and background gray level.     

基于均值偏移的灰度图像分割方法

针对灰度图像分割往往存在的过分割或欠分割问题，提出了一种基于均值偏移的图像分割方法．该方法通过联合像素的空间位置和灰度特征，建立图像的特征矢量，构造基于像素的位置和灰度的改进核函数直方图．采用均值偏移算法搜索图像的特征模式，并以此对图像进行平滑滤波和分割．对以地物为背景的图像分割结果表明，该方法既能抑制具有纹理的大片背景，又能提取出面积较小且轮廓清晰的物体，分割的物体完整且符合人眼视觉，较有效地避免了图像过分割和欠分割的问题．     

Color image edge detection method using VTV denoising and color difference

The traditional Canny edge uses Gaussian filter to suppress the noise, it also smoothes out the image edges. An improved Canny edge detection method for color image is proposed in this paper, the improved method uses fast vectorial total variation (VTV) minimization model to remove noise in color image, and then calculates the color difference and direction in CIELAB color space, which is used for non-maximal suppression. Finally, the improved method extracts the edges by the double-threshold method. The experimental results show that the proposed method achieves better performance than the traditional Canny edge detector. It can remove noise while preserving the image edges, and effectively detect the image edges.     

Noise reduction of speech signals using time-varying and multi-band adaptive gain control for smart digital hearing protectors

In this paper, a single-channel speech enhancement algorithm based on non-linear and multi-band Adaptive Gain Control (AGC) is proposed. The algorithm requires neither Signal-to-Noise Ratio (SNR) nor noise parameters estimation. It reduces the background noise in the temporal domain rather than the spectral domain using a non-linear and automatically adjustable gain function for multi-band AGC. The gain function varies in time and is deduced from the temporal envelope of each frequency band to highly compress the frequency regions where noise is present and lightly compress the frequency regions where speech is present. Objective evaluation using the PESQ (Perceptual Evaluation of Speech Quality) metric shows that the proposed algorithm performs better than three benchmarks, namely: the spectral subtraction, the Wiener filter based on a priori SNR estimation and a band-pass modulation filtering algorithm. In addition, blind subjective tests show that the proposed algorithm introduces less musical noise compared to the benchmark algorithms and was preferred 78.8% of the time in terms of signal quality. The proposed algorithm is implemented in a miniature low power digital signal processor to validate its feasibility and complexity for smart hearing protection in noisy environments.     

基于四波段图像融合的彩色夜视方法研究

为了在低照度条件下获取目标的彩色图像,提高夜间对目标的识别能力,提出了基于四波段图像融合的彩色夜视方法.采用F-P滤光片设计出了透射中心在三基色光中心波长及近红外波段的四波段滤光片,在各个透射区域的平均透射率均达90%以上;将四波段滤光片设计成圆形滤色轮结构,用分光计测得了各波段滤光片的光谱透射性;对加入滤光片后的系统信噪比进行了分析和计算,分析结果表明加入滤光片不会引入噪声,计算结果表明加入蓝色、绿色、红色和近红外波段滤光片的成像系统的信噪比分别是原来单色微光夜视系统信噪比的19.59%、38.45%、47.28%和46.70%.借助国产超二代像增强器在微光实验室进行了四波段图像采集及彩色图像融合实验,实验时光照度分别为1×10~(-3)lx和1×10~(-1)lx,对获取的图像质量进行了评价.结果表明:在照度为1×10~(-3)lx时,融合的彩色图像在均值、方差和熵这三项指标上均优于过滤后的蓝色和绿色的单色图像,且由于彩色图像中利用了近红外图像进行增强,使得彩色图像亮度更高,颜色分辨性更好;在照度为1×10~(-1)lx时,融合后的彩色图像的信息熵比红、绿、蓝三种基色图像的大,彩色图像携带的信息量更大.本文的研究对彩色夜视成像系统的设计和研发具有借鉴和指导意义.     

A general strategy for anisotropic diffusion in MR image denoising and enhancement

Anisotropic diffusion (AD) has proven to be very effective in the denoising of magnetic resonance (MR) images. The result of AD filtering is highly dependent on several parameters, especially the conductance parameter. However, there is no automatic method to select the optimal parameter values. This paper presents a general strategy for AD filtering of MR images using an automatic parameter selection method. The basic idea is to estimate the parameters through an optimization step on a synthetic image model, which is different from traditional analytical methods. This approach can be easily applied to more sophisticated diffusion models for better denoising results. We conducted a systematic study of parameter selection for the AD filter, including the dynamic parameter decreasing rate, the parameter selection range for different noise levels and the influence of the image contrast on parameter selection. The proposed approach was validated using both simulated and real MR images. The model image generated using our approach was shown to be highly suitable for the purpose of parameter optimization. The results confirm that our method outperforms most state-of-the-art methods in both quantitative measurement and visual evaluation. By testing on real images with different noise levels, we demonstrated that our method is sufficiently general to be applied to a variety of MR images.     

Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy

The working environment of wind turbine gearboxes is complex, complicating the effective monitoring of their running state. In this paper, a new gearbox fault diagnosis method based on improved variational mode decomposition (IVMD), combined with time-shift multi-scale sample entropy (TSMSE) and a sparrow search algorithm-based support vector machine (SSA-SVM), is proposed. Firstly, a novel algorithm, IVMD, is presented for solving the problem where VMD parameters (K and α) need to be selected in advance, which mainly contains two steps: the maximum kurtosis index is employed to preliminarily determine a series of local optimal decomposition parameters (K and α), then from the local parameters, the global optimum parameters are selected based on the minimum energy loss coefficient (ELC). After decomposition by IVMD, the raw signal is divided into K intrinsic mode functions (IMFs), the optimal IMF(s) with abundant fault information is (are) chosen based on the minimum envelopment entropy criterion. Secondly, the time-shift technique is introduced to information entropy, the time-shift multi-scale sample entropy algorithm is applied for the analysis of the complexity of the chosen optimal IMF and extract fault feature vectors. Finally, the sparrow search algorithm, which takes the classification error rate of SVM as the fitness function, is used to adaptively optimize the SVM parameters. Next, the extracted TSMSEs are input into the SSA-SVM model as the feature vector to identify the gear signal types under different conditions. The simulation and experimental results confirm that the proposed method is feasible and superior in gearbox fault diagnosis when compared with other methods.     

军用飞机任务保障规划方案效率分析

针对军用飞机再次出动保障任务规划方案的效率分析缺乏有效的解决方法的问题,构建了基于灰色聚类算法与熵权法相结合的任务保障规划方案效率分析模型。该模型首先利用灰色聚类对保障方案的人员利用率的有效参数进行计算并确定。其后利用熵权法对保障方案的人员数量时间分布进行计算。在此基础上根据任务规划工期、人员利用率、人员数量时间分布三个参数的权重得到任务保障规划方案效率。文中通过实例对构建的效率分析模型进行了验证。结果表明：所构建的效率分析模型能够有效的对军用飞机任务保障规划方案效率分析,且具有较高的可靠性和很好的工程应用价值。     

基于数据融合的风电机组噪声预测

根据风电机组噪声信号检测复杂的情况,研究风电机组非声学参数的信息熵特征,对机组噪声进行多源数据融合预测。分析基于信息熵的非声学参数的特征提取方法,并对传统的基于遗传算法的支持向量机回归(GA-SVR)的缺陷提出改进,结合实际应用的非声学参数的信息熵特点平衡遗传算法(GA)的终止条件。通过统计分析完成了输入变量的筛选,去除了对预测影响较大的共线性因素,并实现了输入降维提高预测精度和速率。最后应用数据的信息熵特征,训练改进的GA-SVR建立最终的多源数据特征级融合预测模型。通过对比表明基于多源数据融合的预测方法精度最高,预测结果的相对误差平均值为0.7757%,具有实际可行性。     

基于蚁群算法的二维最大熵分割算法

由于二维最大熵分割法不仅考虑了像素的灰度信息,而且还充分利用了像素的空间邻域信息,因此能够取得较好的分割效果.但是,该方法的计算量巨大,不利于红外图像的快速处理.蚁群算法于20世纪90年代初提出,是受到蚁群集体行为的启发而提出的一种基于种群的模拟进化算法,属于随机搜索算法.该算法已经成功应用于旅行商等离散问题.将蚁群算法应用于二维最大熵法,提出了基于蚁群算法的二维最大熵分割算法.与传统的穷尽搜索法相比,求解速度提高了60倍左右.仿真实验表明,该方法快速、简单、有效.     

An MRI denoising method using image data redundancy and local SNR estimation

This paper presents an LMMSE-based method for the three-dimensional (3D) denoising of MR images assuming a Rician noise model. Conventionally, the LMMSE method estimates the noise-less signal values using the observed MR data samples within local neighborhoods. This is not an efficient procedure to deal with this issue while the 3D MR data intrinsically includes many similar samples that can be used to improve the estimation results. To overcome this problem, we model MR data as random fields and establish a principled way which is capable of choosing the samples not only from a local neighborhood but also from a large portion of the given data. To follow the similar samples within the MR data, an effective similarity measure based on the local statistical moments of images is presented. The parameters of the proposed filter are automatically chosen from the estimated local signal-to-noise ratio. To further enhance the denoising performance, a recursive version of the introduced approach is also addressed. The proposed filter is compared with related state-of-the-art filters using both synthetic and real MR datasets. The experimental results demonstrate the superior performance of our proposal in removing the noise and preserving the anatomical structures of MR images.     

非局部变分修复法去除高密度椒盐噪声

分析了中值滤波及其改进型算法在处理高密度椒盐噪声时效果不理想的原因,采用变分修复方法来去除高密度椒盐噪声,基于现有的全变差修复模型提出了非局部全变差修复模型。该模型利用椒盐噪声特点(均匀分布、灰度值为0或255),将噪声点看成是图像中遗失或是破损的点,首先在图像中寻找与噪声点邻域相似的区域,将相似区域的中心像素作为噪声点新的邻域然后对其插值,把图像降噪问题转化为图像修复问题,从而达到去除高密度噪声的目的。实验结果表明:该模型对噪声密度为90%的彩色和灰度图像去噪后,其峰值信噪比为22.85和28.77,在客观评价标准方面优于中值滤波及其改进型算法。该模型能有效去除高密度下的椒盐噪声并较好地恢复图像细节,为图像去除高密度噪声提供了一种新的途径。     

基于多特征差异检测与联合控制映射的红外图像选择加密算法

为了实现对红外图像的选择性加密,提出了基于多特征差异检测与联合控制映射的红外图像选择算法。引入分段正弦变换,将输出图像分割为3个不同的区域,对每个区域完成不同的拉伸变换,完成初始红外图像的增强,凸显真实红外目标;再利用增强图像中目标与背景的灰度差异,从而设计目标决策因子,并分割Top-Hat变换的结构元素,构建红外背景抑制机制,过滤杂波与噪声;利用灰度水平、对比度与相似度,建立多特征差异检测模型,提取包含真实目标与可疑目标的感兴趣区域;以Logistic映射为控制条件,综合Tent映射与Chebyshev映射,设计联合控制混沌映射,利用其输出的混合随机序列对感兴趣区域进行置乱;引入引力模型,对混淆的感兴趣区域内的像素进行扩散,完成红外目标选择加密。实验结果显示:与已有的图像局部加密机制相比,该文算法输出密文信息熵值达到了7.982 6,能够更好地用于红外图像局部选择性加密。     

Further improvement of edge location accuracy of double fiber spherical coupling sensor using orthogonal Jacobi–Fourier moments

In order to further improve the edge location accuracy of double fiber spherical coupling sensor, an approach based on OJFMs is proposed. In this paper, OJFMs with high order spatial moment and simple calculation is built. Then, its edge location method and fast discrete algorithm is proposed. Finally, the proposed method is tested by locating the edge of standard pattern and the image acquired by double fiber spherical coupling sensor with lot of noise. Experiment results show that the location accuracy is up to 0.05 pixel and 0.08 pixel for standard lines and standard circle, respectively. The accuracy of double fiber spherical coupling sensor is 0.026 pixel in 2σ band and a actual accuracy of 36 nm is obtained by OJFMs after a moving average filter (N = 7).     

Motion blur parameters estimation for image restoration

This paper deals with estimation of parameters for motion blurred images. The objectives are to estimate the length (L) and the blur angle (θ) of the given degraded image as accurately as possible so that the restoration performance can be optimised. Gabor filter is utilized to estimate the blur angle whereas a trained radial basis function neural network (RBFNN) estimates the blur length. Once these parameters are estimated the conventional restoration is performed. To validate the proposed scheme, simulation has been carried out on standard images as well as in real images subjected to different blur angles and lengths. The robustness of the scheme is also validated in noise situations of different strengths. In all situations, the results have been compared with standard schemes. It is in general observed that the proposed scheme outperforms its counterparts in terms of restoration parameters and visual quality.     

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.