基于结构张量的Non-Local Means去噪算法研究

非局部平均是当前一种新兴而有效的图像去噪方法。为了能充分利用数字图像局部几何结构的自相似性,同时由于结构张量可有效刻画数字图像的局部几何结构特征,进而提出了基于结构张量相似性度量的非局部平均去噪算法。实验结果验证了该算法抑制噪声的有效性,同时能很好地保持边缘等细节特征,峰值信噪比得到有效提高。

Local edge direction based non-local means for image denoising

Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.

基于Non-Local means滤波的雾天降质图像恢复算法

针对目前去雾算法易导致边缘晕环效应、边缘轮廓及景物特征比较模糊问题,提出了一种景深等先验信息未知条件下基于Non-Local means滤波的雾天降质图像恢复算法。首先,根据大气散射模型将经典的场景深度估计转化为大气面纱以及天空亮度估计,避免难求的场景深度图;然后,对雾天降质图像进行雾气平均化预处理,经过预处理图像平均亮度变小;其次,依据大气面纱的边缘跟雾天图像的低频具有大的相似性,采用Non-Localmeans滤波算法估计大气面纱模型;最后,为了使恢复图像的亮度跟色度都更加接近晴天图像,进行防止对比度放大的平滑与色度调整处理。通过与已有实验结果对比表明,提出的算法可以获得更精确的大气面纱,恢复图像不但边缘轮廓及景物特征都比较清楚,而且可有效抑制边缘晕环效应。

Validity of non-local mean filter and novel denoising method

Background Image denoising is an important topic in the digital image processing field.This study theoretically investigates the validity of the classical nonlocal mean filter(NLM)for removing Gaussian noise from a novel statistical perspective.Method By considering the restored image as an estimator of the clear image from a statistical perspective,we gradually analyze the unbiasedness and effectiveness of the restored value obtained by the NLM filter.Subsequently,we propose an improved NLM algorithm called the clustering-based NLM filter that is derived from the conditions obtained through the theoretical analysis.The proposed filter attempts to restore an ideal value using the approximately constant intensities obtained by the image clustering process.In this study,we adopt a mixed probability model on a prefiltered image to generate an estimator of the ideal clustered components.Result The experiment yields improved peak signal-to-noise ratio values and visual results upon the removal of Gaussian noise.Conclusion However,the considerable practical performance of our filter demonstrates that our method is theoretically acceptable as it can effectively estimate ideal images.

Fast Non-Local Means Algorithm Based on Krawtchouk Moments

Non-local means(NLM)method is a state-of-the-art denoising algorithm, which replaces each pixel with a weighted average of all the pixels in the image. However, the huge computational complexity makes it impractical for real applications. Thus, a fast non-local means algorithm based on Krawtchouk moments is proposed to improve the denoising performance and reduce the computing time. Krawtchouk moments of each image patch are calculated and used in the subsequent similarity measure in order to perform a weighted averaging. Instead of computing the Euclidean distance of two image patches, the similarity measure is obtained by low-order Krawtchouk moments, which can reduce a lot of computational complexity. Since Krawtchouk moments can extract local features and have a good antinoise ability, they can classify the useful information out of noise and provide an accurate similarity measure. Detailed experiments demonstrate that the proposed method outperforms the original NLM method and other moment-based methods according to a comprehensive consideration on subjective visual quality, method noise, peak signal to noise ratio(PSNR), structural similarity(SSIM) index and computing time. Most importantly, the proposed method is around 35 times faster than the original NLM method.

Improved Non-Local Means Algorithm for Image Denoising

Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, an improved weighted non-local means algorithm for image denoising is proposed. The non-local means denoising method replaces each pixel by the weighted average of pixels with the surrounding neighborhoods. The proposed method evaluates on testing images with various levels noise. Experimental results show that the algorithm improves the denoising performance.

Two Modifications of Weight Calculation of the Non-Local Means Denoising Method

The non-local means (NLM) denoising method replaces each pixel by the weighted average of pixels with the sur-rounding neighborhoods. In this paper we employ a cosine weighting function instead of the original exponential func-tion to improve the efficiency of the NLM denoising method. The cosine function outperforms in the high level noise more than low level noise. To increase the performance more in the low level noise we calculate the neighborhood si-milarity weights in a lower-dimensional subspace using singular value decomposition (SVD). Experimental compari-sons between the proposed modifications against the original NLM algorithm demonstrate its superior denoising per-formance in terms of peak signal to noise ratio (PSNR) and histogram, using various test images corrupted by additive white Gaussian noise (AWGN).

A Robust and Fast Non-Local Means Algorithm for Image Denoising

In the paper, we propose a robust and fast image denoising method. The approach integrates both Non- Local means algorithm and Laplacian Pyramid. Given an image to be denoised, we first decompose it into Laplacian pyramid. Exploiting the redundancy property of Laplacian pyramid, we then perform non-local means on every level image of Laplacian pyramid. Essentially, we use the similarity of image features in Laplacian pyramid to act as weight to denoise image. Since the features extracted in Laplacian pyramid are localized in spatial position and scale, they are much more able to describe image, and computing the similarity between them is more reasonable and more robust. Also, based on the efficient Summed Square Image （SSI） scheme and Fast Fourier Transform （FFT）, we present an accelerating algorithm to break the bottleneck of non-local means algorithm - similarity computation of compare windows. After speedup, our algorithm is fifty times faster than original non-local means algorithm. Experiments demonstrated the effectiveness of our algorithm.

The Algorithms about Fast Non-local Means Based Image Denoising

Image denoising is still a challenge of image processing. Buades et al. proposed a nonlocal means （NL-means） approach. This method had a remarkable denoising results at high expense of computational cost. In this paper, We compared several fast non-local means methods, and proposed a new fast algorithm. Numerical experiments showed that our algorithm considerably reduced the computational cost, and obtained visually pleasant images.

A Two-Step Regularization Framework for Non-Local Means

As an effective patch-based denoising method, non-local means （NLM） method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The in, plementation of NLM can formally be decomposed into two sequential steps, i.e., computing the weights and using the weights to compute the weighted means. In the first step, the weights can be obtained by solving a regularized optimization. And in the second step, the means can be obtained by solving a weighted least squares problem. Motivated by such observations, we establish a two-step regularization framework for NLM in this paper. Meanwhile, using the fl-amework, we reinterpret several non-local filters in the unified view. Further, taking the framework as a design platform, we develop a novel non-local median filter for removing salt-pepper noise with encouraging experimental results.

Long radial coherence of electron temperature fluctuations in non-local transport in HL-2A plasmas

The dynamics of long-wavelength(kθ<1.4 cm^(-1)),broadband(20 kHz–200 kHz)electron temperature fluctuations(Te/Te)of plasmas in gas-puff experiments are observed for the first time in HL-2A tokamak.In a relatively low density(ne(0)■0.91×10^(19)m^(-3)–1.20×10^(19)m^(-3))scenario,after gas-puffing the core temperature increases and the edge temperature drops.On the contrary,temperature fluctuation drops at the core and increases at the edge.Analyses show the non-local emergence is accompanied with a long radial coherent length of turbulent fluctuations.While in a higher density(ne(0)?1.83×10^(19)m^(-3)–2.02×10^(19)m^(-3))scenario,the phenomena are not observed.Furthermore,compelling evidence indicates that E×B shear serves as a substantial contributor to this extensive radial interaction.This finding offers a direct explanatory link to the intriguing core-heating phenomenon witnessed within the realm of non-local transport.

Dynamic behavior of rectangular crack in three-dimensional orthotropic elastic medium by means of non-local theory

The dynamic behavior of a rectangular crack in a three-dimensional （3D） orthotropic elastic medium is investigated under a harmonic stress wave based on the non-local theory. The two-dimensional （2D） Fourier transform is applied, and the mixed- boundary value problems are converted into three pairs of dual integral equations with the unknown variables being the displacement jumps across the crack surfaces. The effects of the geometric shape of the rectangular crack, the circular frequency of the incident waves, and the lattice parameter of the orthotropic elastic medium on the dynamic stress field near the crack edges are analyzed. The present solution exhibits no stress singularity at the rectangular crack edges, and the dynamic stress field near the rectangular crack edges is finite.

A powerful denoising method based on non-local means filter for cryo-electron microscopic images

Cryo-electron microscopic images of biological molecules usually have high noise and low contrast. It is essential to suppress noise and enhance contrast in order to recognize

Fresnel Equations Derived Using a Non-Local Hidden-Variable Particle Theory

Problem: The Fresnel equations describe the proportions of reflected and transmitted light from a surface, and are conventionally derived from wave theory continuum mechanics. Particle-based derivations of the Fresnel equations appear not to exist. Approach: The objective of this work was to derive the basic optical laws from first principles from a particle basis. The particle model used was the Cordus theory, a type of non-local hidden-variable (NLHV) theory that predicts specific substructures to the photon and other particles. Findings: The theory explains the origin of the orthogonal electrostatic and magnetic fields, and re-derives the refraction and reflection laws including Snell’s law and critical angle, and the Fresnel equations for s and p-polarisation. These formulations are identical to those produced by electromagnetic wave theory. Contribution: The work provides a comprehensive derivation and physical explanation of the basic optical laws, which appears not to have previously been shown from a particle basis. Implications: The primary implications are for suggesting routes for the theoretical advancement of fundamental physics. The Cordus NLHV particle theory explains optical phenomena, yet it also explains other physical phenomena including some otherwise only accessible through quantum mechanics (such as the electron spin g-factor) and general relativity (including the Lorentz and relativistic Doppler). It also provides solutions for phenomena of unknown causation, such as asymmetrical baryogenesis, unification of the interactions, and reasons for nuclide stability/instability. Consequently, the implication is that NLHV theories have the potential to represent a deeper physics that may underpin and unify quantum mechanics, general relativity, and wave theory.

基于K-means/RPF的大型遮蔽空间人员定位算法

针对大型遮蔽空间惯性/地图匹配算法中粒子贫化和子粒子群迷路效应导致定位精度下降的问题,提出一种基于K-means聚类的回溯粒子滤波(RPF)人员定位算法。首先,用行人航位推算(PDR)中航向更新、步频检测及步长估计得到初始运动轨迹;然后,设计RPF算法,提高存活粒子有效性和多样性,缓解粒子贫化,提高人员定位精度;最后,通过K-means聚类算法解决子粒子群的迷路效应,修正人员轨迹出现在非可行域的现象。实验结果表明:本文算法抑制了粒子贫化和子粒子群迷路效应,人员平均定位误差相比惯性定位和标准粒子滤波降低了81.20%和51.48%。

基于K-Means++算法和改进遗传算法的维保站维修调度方法的研究

传统的电梯维保工作模式是定期保养,即针对每台电梯而言,需要每半个月去保养一次,然而电梯设备的分布和保养进度的复杂性决定了人为排定的维保路线不能够最大限度地解决维保资源。为此提出基于K-Means++算法和改进遗传算法的维保站维修调度方法,首先使用分解法的思想将问题进行拆分,然后采用K均值聚类算法将维保任务分配到合适的维保站,建立总路程最短的维保调度模型,最后利用改进遗传算法进行求解,获取最优路线规划结果,对缩减维保工作时间,提升维保工作效率,从而提升电梯使用的安全性和可靠性有一定的意义。

基于改进K-means聚类算法的分布式储能集群划分方法

随着规模化分布式电源及储能的接入,配电网的功率返送、节点过电压等问题愈加显著,对电网规划、运行监视和调度控制等造成一定影响,也不利于储能大范围发展。为此提出一种适用于规模化分布式储能的集群划分方法,基于功率节点电压灵敏度的电气距离模块度指标,对经典K-means算法进行改进,设计节点指数法、肘部法则优化初始聚类中心选择和集群数确定。以IEEE33系统算例进行验证,结果表明所提集群划分方法具有较强的电气耦合性、准确性和运算效率。

Non-Local DWI Image Super-Resolution with Joint Information Based on GPU Implementation

Since the spatial resolution of diffusion weighted magnetic resonance imaging(DWI)is subject to scanning time and other constraints,its spatial resolution is relatively limited.In view of this,a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution.Based on the non-local strategy,we use the joint information of adjacent scan directions to implement a new weighting scheme.The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI.However,the algorithm ran slowly because of the joint information.In order to apply the algorithm to the actual scene,we compare the proposed algorithm on CPU and GPU respectively.It is found that the processing time on GPU is much less than on CPU,and that the highest speedup ratio to the traditional algorithm is more than 26 times.It raises the possibility of applying reconstruction algorithms in actual workplaces.

Mean Shift跟踪算法创新实验项目设计

视频跟踪算法是计算机视觉实践课程中比较受关注的实验项目。针对突变情况下传统Mean Shift跟踪算法无法实时准确跟踪的问题,设计了基于模板更新和线性预估的Mean Shift跟踪算法创新实验项目。在模板更新策略下,引入背景模板,通过将原目标模板和背景模板与设定的阈值进行比较来对干扰因素进行判定,当干扰因素判定目标受到遮挡时,引入线性预估方程进行目标位置预测,有效解决目标在遮挡情况下跟踪丢失的问题。通过对测试视频的跟踪效果和性能进行对比分析,验证了算法在突变情况下相较于传统算法具有更好的抗干扰能力。以算法创新设计为核心,通过开放性创新实验项目的选题、设计、答辩、反馈的闭环实验过程,有效提高了学生算法创新设计能力。

基于特征分箱和K-Means算法的用户行为分析方法

针对网购用户所产生的购物行为进行分析,首先通过数据处理构建客户关系管理模型(RFM模型),在此模型的基础上采用特征分箱法和K-Means聚类两种方法对用户进行细分,并对2种模型结果进行比较分析,讨论二者的差异性和具体的应用范围和意义.其中,基于特征分箱法的RFM模型将变量转化到相似的尺度上并将变量离散化,使得用户分类标签更加清晰,也可依据各类标签分类出不同类型的用户.K-Means算法通过轮廓系数评估聚类算法质量以至于选取最优K值.本文实验分析结果可为运营商提供更加可靠直观的数据,使得运营商可以根据不同用户的不同行为进行市场细分,进而进行精准营销和服务设置.