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。

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.

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.

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).

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.

基于云模型和k-means聚类的风电场储能容量优化配置方法

合理确定风电场侧储能系统容量配置方案是实现风电输出功率波动有效平抑的关键问题。针对传统k-means聚类算法难以给定聚类数目且算法稳定性较差的问题,在采用自适应小波包分解法处理风电输出得到储能运行曲线的基础上,基于云模型理论将储能充放电功率的概率分布分解成若干个正态云模型的叠加,根据数据特性自动确定聚类数目和初始聚类中心,然后应用kmeans聚类算法从储能运行曲线中聚合出具有代表性的充放电曲线集合作为储能容量优化模型的输入,从而最终确定储能系统的配置方案。仿真结果验证了所提算法的合理性和稳定性。

基于NL-Means的均值平移图像分割算法

针对均值平移图象分割算法中,在密度中心点选择时的不足,本文采用一种新的寻找密度中心点的方法,同时,为了克服传统基于特征空间分析的图像分割方法对像素点空间关系考虑不够充分的缺陷,通过Non-local means算法,在距离公式中引入特征权参数,从而优化聚类效果。对图象分割结果分析表明了这种方法的有效性。

基于L0梯度最小化和K-Means聚类的织物缺陷检测研究

为了控制产品质量,保障织物的美观和舒适性,针对织物表面的缺陷,课题组提出了一种基于L0梯度最小化和K-Means聚类的缺陷检测方法。主要分为2个步骤:首先,使用L0梯度最小化将缺陷图像进行平滑,去除背景纹理的影响,保留图像较大的边缘;然后,使用K-Means聚类对平滑后的图像进行聚类,从而分割出缺陷区域。将该检测方法用于纺织厂收集的缺陷图像上进行验证,实验结果表明该方法能准确地检测出织物表面缺陷。该项研究提高了检测效率,满足织物生产的要求。

Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

动态粒子群优化K-means的图像分割算法研究

K-means聚类算法在图像分割领域中的运用越来越普遍,但由于K-means算法对噪声具有敏感性,对初始聚类中心具有依赖性,并且容易收敛至局部最优解,使其在图像分割时效果并不是很理想,对此提出一种改进的结合动态粒子群优化与K-means聚类的混合算法来优化图像分割的效果。首先利用双边滤波进行平滑降噪处理,再通过动态调整惯性系数来提高PSO算法的全局优化能力,随后将动态粒子群优化的输出结果作为K-means算法的初始聚类中心,最后通过多次迭代直至收敛。实验结果表明,新算法能有效提升图像分割效果与分割质量。

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.

一种改进的K-means聚类算法在图像分割中的应用

K-means聚类算法是图像分割中比较常见的一种方式。它是一种无监督学习方法,能够从研究对象的特征中发现关联规则,因而具有强有力的处理方法。但是,由于该算法对噪声的敏感性K值及初始类心的不确定性,使其在图像分割中存在缺陷,于是提出了一种改进的K-means聚类算法来提高分割的效果。首先对图像进行平滑滤波处理,再根据相应条件找到特征向量作为初始类心,最后进行聚类操作。实验表明,本算法能够有效提取目标对象,提高图像分割的效果。

Automatic segmentation of brain tissue based on improvedfuzzy c means clustering algorithm

In medical images, exist often a lot of noise, the noise will seriously affect the accuracy of the segmentation results. The traditional standard fuzzy c-means(FCM) algorithm in image segmentation do not taken into account the relationship the adjacent pixels, which leads to the standard fuzzy c-means(FCM) algorithm is very sensitive to noise in the image. Proposed improvedfuzzy c-means(FCM) algorithm, taking both the local and non-local information into the standard fuzzy c-means(FCM) clustering algorithm. The ex-periment results can show that the improved algorithm can achieve better effect than other methods of brain tissue segmentation.

平滑去噪分块K-means算法的机器人视觉图像处理

针对机器人视觉目标图像信噪比低、背景噪声干扰大的特点,采用马尔科夫随机场(Markov Random Field,MRF)模型的平滑去噪方法对图像进行预处理。在此基础上,采用K-means聚类算法对图像进行聚类,将具有不同特征的目标区域分类,为进一步实现目标识别和跟踪提供基础。同时,为进一步克服移动机器人导航过程中视觉处理速度慢的缺陷,对图像进行分块划分,提取每个图像块的均值、方差和最大值作为特征值,从而提高算法的处理速度。

结合二次核函数的Mean Shift图像平滑

针对Mean Shift算法在图像平滑过程中由于过平滑现象而导致平滑区域易出现边缘模糊问题,提出一种基于二次核函数的Mean Shift图像平滑算法,该算法利用核函数对采样点加权,通过Mean Shift向量迭代至灰度概率密度最大处,并将此灰度值赋予当前像素点,依次遍历每个像素点,不断聚类对图像进行平滑.此外,在四幅标准图像上对算法进行了仿真实验.并在视觉效果和量化评价等方面,与基于另外四种核函数的Mean Shift图像平滑算法进行了实验比较.实验结果表明,本文算法在最大限度地平滑掉图像多余细节和噪声的同时,能够保证图像被平滑区域的边缘不被模糊.

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.

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