均值滤波算法研究

介绍了经典非局部均值滤波算法与Manjón非局部均值滤波算法,改进了非局部均值滤波方法的相似度权值,使算法在具有旋转平移不变性,保持时间复杂度的同时优化了视觉效果与信噪比。实验通过添加噪声标准差从10~100不等的高斯加性噪声,比较了改进后的算法与传统滤波算法以及Manjón非均值滤波算法,结果表明,改进后的算法无论从视觉上还是数值上都优于Manjón非均值滤波算法。     

基于非局部自相似性的混合噪声滤波算法

针对现有滤波算法滤除图像中高斯-椒盐混合噪声后细节丢失较严重的问题,将图像块自相似性思想引入到混合噪声滤波领域,提出了基于非局部自相似性的混合噪声滤波算法。设计了图像在含混合噪声的情况下,其块相似度衡量策略及权系数的计算方式,实现了利用图像自相似性对椒盐噪声进行滤除,再结合非局部均值滤波算法处理图像中剩余的高斯噪声。实验结果表明,提出的算法可以有效地滤除图像中的混合噪声并较大程度地保留边缘、纹理等图像细节信息。     

基于主特征提取的Retinex多谱段图像增强

根据Retinex视觉模型中照射分量和反射分量的统计特性,融合多尺度主特征提取法、平台直方图算法、非局部均值滤波及局部细节增强算法可对多谱段图像进行有效增强.首先利用多尺度主特征提取法估计照射分量,对照射分量进行平台直方图操作,增强全局对比度及图像主结构边缘细节;然后将原图与照射分量相除获取反射分量,对反射分量进行非局部均值滤波抑制噪声,再进行基于局部方差的局部细节增强;最后将增强后的照射分量与反射分量相乘,即为增强图像.从主观和客观两方面,对X光图像、紫外图像、可见光图像、低照度可见光图像和红外图像实验结果的分析表明,本文算法能够有效地抑制图像噪声、增强图像对比度及细节、改善图像视觉效果,是一种通用有效的多谱段图像增强算法.     

基于改进非局部均值的CPMG信号去噪算法

采用低场核磁共振技术进行检测时,接收到的回波信号微弱且信噪比低,真实的信号容易淹没在背景噪声中,严重影响到后续的反演等操作的准确性．针对这一问题,提出利用非局部均值滤波算法对CPMG(Carr Purcell Meiboom Gill)回波信号进行降噪的方法．首先,对算法中至关重要的参数选择的方法进行分析,提出了利用Stein无偏风险估计的自适应参数选取方法;然后,根据回波信号的特性对算法进行改进,即利用信号点数据方差的不同,自适应地求取各点进行非局部均值滤波时的相似窗宽度;最后,求取利用最优参数进行降噪后的CPMG回波信号．对仿真数据和真实数据的反演结果对比分析表明,该改进的非局部均值滤波算法能够取得更好的滤波效果,能够获得较优的反演谱．     

基于非局部均值和SUSAN算子的混合噪声滤除

为了更好地滤除图像中脉冲噪声和高斯噪声组成的混合噪声,提出了一种基于非局部均值和Small Univalue Segment Assimilating Nucleus(SUSAN)算子的混合噪声滤除方法.该方法首先根据脉冲噪声点与角点之间吸收核同值区形状特征的不同,采用SUSAN算子检测出大量的特征点,特征点主要是脉冲噪声点,也可能含有小部分角点.将特征点进行排序,出现频次最高两位的点为脉冲噪声点.然后采用改进的均值滤波法计算脉冲噪声点邻域中非脉冲噪声点的均值,以此替换脉冲噪声点灰度值.最后针对已滤除脉冲噪声的图像,采用考虑了图像块信息的非局部均值方法滤除剩余的高斯噪声.去噪实验结果表明:与自适应中值和加权均值结合的方法、中值滤波与小波结合的方法、脉冲耦合神经网络与中值滤波结合的方法相比,本文方法主观视觉效果更好,能够更好地保留图像中的边缘细节,客观评价指标峰值信噪比有较大的提高,滤除混合噪声的优势明显.     

基于小波变换的图像混合噪声自适应滤除算法

为同时滤除图像中的椒盐噪声和高斯噪声,提出了一种基于小波变换的混合噪声自适应滤除算法,该算法首先采用中值滤波去除椒盐噪声,然后借助边缘检测算子区将图像为分边缘与非边缘区域,进一步对非边缘区域引入改进的均值滤波器,有效削弱高斯噪声的同时保护图像边缘细节,既初步削弱高斯噪声又保护了边缘,最后采用改进的小波阈值滤波算法,对不同的小波系数采用不同的阈值函数,通过线性回归得到各最优阈值关系式。实验结果表明,该混合噪声自适应滤除算法能有效滤除椒盐噪声和高斯噪声,在图像主观质量和客观质量上均取得了较好的效果,能提高去噪图像峰值信噪比0.5~2.0 dB。     

改进的中值滤波算法在图像去噪中的应用

 针对标准中值滤波方法存在的不足,结合均值思想提出两种改进的中值滤波算法,即加权快速中值滤波算法和加权自适应中值滤波算法,MATLAB实验证实两种方法均能更好地保存原始图像的细节和边缘。比较两种新方法得出以下结论：加权改进中值滤波算法对低密度的脉冲噪声去噪效果明显,对于高密度脉冲噪声去噪效果不理想,但能大大提高中值滤波的运行速度,对数字图像实时处理意义很大;加权自适应中值滤波算法能够有效地消除被污染图像中的高密度脉冲噪声,较标准中值滤波具有更优良的滤波性能,较加权快速中值滤波算法在去噪方面有更好的鲁棒性。     

视频车辆监控系统图像滤波算法的研究

视频车辆监控系统中图像噪声对背景学习、车辆识别等具有很大影响,已有滤波算法又难以满足系统对滤波效果和速度的综合要求。通过对比不滤波、采用中值滤波或均值滤波时的车辆识别效果和速度,选用均值滤波作为进一步的研究对象。在适当减少计算量的基础上,通过改进数据访问方法和计算方法进一步提高了算法速度,并用Visual C++ 6.0实现了该算法。实验表明,新算法的速度比盒滤波提高了55 %。     

基于均值漂移实现非发射线天体光谱的谱线自动提取

非发射线天体的光谱是天体光谱谱线提取中最难处理的一种。针对非发射线天体,给出了一种基于均值漂移的谱线自动提取方法。首先,利用均值漂移总是指向局部密度最大点也即密度的模式点这一性质,提取出较为满意的伪连续谱;其次,均值漂移滤波同时工作在空间域和幅度域上,是一种非线性的边缘保持滤波方法,在去除噪声的同时,能够较好地保持特征谱线的信息,文章在连续谱归一化后,采用均值漂移去噪得到谱线光谱;最后,对谱线光谱设置局部阈值提取出特征谱线。通过对恒星、正常星系等的实验表明：该方法是有效的, 这将对后续的参数测量和基于谱线的光谱分类非常有利。     

基于非局部均值滤波与时域高通滤波的非均匀性校正算法

时域高通滤波非均匀性校正是一种典型的基于场景的红外焦平面阵列非均匀性校正算法,但其易产生"鬼影"现象,影响校正效果.本文在时域高通滤波校正算法的原理基础上,分析了其校正过程中"鬼影"现象产生的原因,即由于全部图像信息的叠加而导致静止场景被滤除且运动场景会在当前位置留下反转的图像,从而形成"鬼影".引入非局部均值滤波方法,提出了一种去"鬼影"的非局部均值滤波-时域高通滤波非均匀性校正方法.该方法首先采用非局部均值滤波将图像信息分离成高低频两部分(其中高频成分含有大部分噪音及非均匀性),并使用高频成分进行时域高通滤波算法中低通输出的递归运算,使得低通滤波后的图像含有较少的场景信息,从而可使校正输出图像含有较少的"鬼影"现象.采用两组真实红外序列图像进行验证,结果表明该算法不仅能获得较好的非均匀性校正效果,而且能较好地抑制时域高通滤波算法中的"鬼影"现象.     

Switching non-local vector median filter

This paper describes a novel image filtering method that removes random-valued impulse noise superimposed on a natural color image. In impulse noise removal, it is essential to employ a switching-type filtering method, as used in the well-known switching median filter, to preserve the detail of an original image with good quality. In color image filtering, it is generally preferable to deal with the red (R), green (G), and blue (B) components of each pixel of a color image as elements of a vectorized signal, as in the well-known vector median filter, rather than as component-wise signals to prevent a color shift after filtering. By taking these fundamentals into consideration, we propose a switching-type vector median filter with non-local processing that mainly consists of a noise detector and a noise removal filter. Concretely, we propose a noise detector that proactively detects noise-corrupted pixels by focusing attention on the isolation tendencies of pixels of interest not in an input image but in difference images between RGB components. Furthermore, as the noise removal filter, we propose an extended version of the non-local median filter, we proposed previously for grayscale image processing, named the non-local vector median filter, which is designed for color image processing. The proposed method realizes a superior balance between the preservation of detail and impulse noise removal by proactive noise detection and non-local switching vector median filtering, respectively. The effectiveness and validity of the proposed method are verified in a series of experiments using natural color images.     

Two-stage non-local means filtering with adaptive smoothing parameter

Non-local means (NLM) filtering is an efficacious algorithm in image denoising which searches the similar neighborhoods and estimates the pixel by averaging these neighborhoods. Some internal parameters such as patch size, search window size and smoothing strength have serious effects on filtering performance. This paper proposes an improved version of NLM by using weak textured patches based single image noise estimation and two-stage NLM with adaptive smoothing parameter. Our proposed method firstly applies weak textured patches based noise estimation to achieve the noise level of input noisy image. Then relying on the estimated noise level, we apply the first stage NLM with adaptive smoothing parameter to attain a basic denoised image. After that, the basic denoised image is refined by the second stage of NLM with smaller smoothing strength. Our experimental results show that the proposed algorithm outperforms the NLM and some NLM recent variants both in visual quality and numerical measures. Additionally, the potential halo effect is almost eliminated in the result images produced by our proposed method.     

Modifying NL-means to a universal filter

Despite a state-of-the-art filter for removing Gaussian noise, non-local means filter (NLM), like its local counterpart (the mean filter), is no longer so effective in removing salt-pepper noise which is common in real world as well. By contrast, adaptive median filter (AMF) is concise and can remove this type of noise effectively. Inspired by the AMF filtering strategies, in this paper, we modify NLM to a novel non-local universal filter (UNLM) which can remove not only either of Gaussian noise and salt-pepper noise but also their mixture. Experiments on artificial and benchmark images validate its feasibility and effectiveness.     

Image enhancement algorithm based on improved lateral inhibition network

There is often substantial noise and blurred details in the images captured by cameras. To solve this problem, we propose a novel image enhancement algorithm combined with an improved lateral inhibition network. Firstly, we built a mathematical model of a lateral inhibition network in conjunction with biological visual perception; this model helped to realize enhanced contrast and improved edge definition in images. Secondly, we proposed that the adaptive lateral inhibition coefficient adhere to an exponential distribution thus making the model more flexible and more universal. Finally, we added median filtering and a compensation measure factor to build the framework with high pass filtering functionality thus eliminating image noise and improving edge contrast, addressing problems with blurred image edges. Our experimental results show that our algorithm is able to eliminate noise and the blurring phenomena, and enhance the details of visible and infrared images.     

Single infrared image super-resolution combining non-local means with kernel regression

In many infrared imaging systems, the focal plane array is not sufficient dense to adequately sample the scene with the desired field of view. Therefore, there are not enough high frequency details in the infrared image generally. Super-resolution (SR) technology can be used to increase the resolution of low-resolution (LR) infrared image. In this paper, a novel super-resolution algorithm is proposed based on non-local means (NLM) and steering kernel regression (SKR). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of high-resolution (HR) pixel can be estimated. SKR method is derived based on the local smoothness of the natural images. In this paper the SKR is used to give the regularization term which can restrict the image noise and protect image edges. The estimated SR image is obtained by minimizing a cost function. In the experiments the proposed algorithm is compared with state-of-the-art algorithms. The comparison results show that the proposed method is robust to the noise and it can restore higher quality image both in quantitative term and visual effect.     

基于边窗盒子滤波和透射率修正的图像去雾

针对雾线先验去雾算法存在的颜色过饱和现象、图像初始透射率估算不准确等问题，提出了一种基于边窗盒子滤波和透射率修正的图像去雾算法。为了解决初始透射率估算不准确带来的边缘细节信息丢失的问题，首先利用非局部总广义变分（TGV）正则化的方法估算初始透射率，并将二阶的非局部总广义变分（TGV）正则器来作为正则项，以确保对由图像颜色和深度之间的噪声和歧义引起的异常值具有鲁棒性。随后利用边窗滤波算法对初始透射率进行优化，从而实现对图像中纹理信息和边缘信息的保留。最后利用大气散射模型和多角度优化后的透射率复原出无雾的原始图像。实现结果表明，本文算法能够解决图像颜色过饱和与边缘处的细节纹理信息丢失的问题，且无色调偏移和光晕效应。在定性评估上，复原后的图像视觉效果好；在定量评估上，本文算法的去雾后图像的评价指标皆高于基于雾线先验算法。     

Reconstruction method of computational ghost imaging based on non-local generalized total variation北大核心CSCD

鬼成像是一种能够透过大雾等恶劣环境的成像技术。针对传统鬼成像重建图像存在噪声较多、图像对比度较低等问题,将非局部广义全变分方法用于鬼成像的图像重建之中,提出基于非局部广义全变分的计算鬼成像重建方法。所提方法构造了一种非局部相关性权重设计梯度算子,将其代入全变分重建算法中,使得重建的图像能有效去除噪声的同时实现细节较好的还原。首先在不同条件下进行仿真模拟,得到所提方法的峰值信噪比相对其他方法提升1 dB左右,且具有更好的主观视觉效果,进而设计并搭建实验平台对算法的有效性进行验证,实验结果证明了所提方法在去除噪声和细节重建等方面的优越性。     

Rician noise reduction in magnetic resonance images using adaptive non-local mean and guided image filtering

This paper proposes a Rician noise reduction method for magnetic resonance (MR) images. The proposed method is based on adaptive non-local mean and guided image filtering techniques. In the first phase, a guidance image is obtained from the noisy image through an adaptive non-local mean filter. Sobel operators are applied to compute the strength of edges which is further used to control the spread of the kernel in non-local mean filtering. In the second phase, the noisy and the guidance images are provided to the guided image filter as input to restore the noise-free image. The improved performance of the proposed method is investigated using the simulated and real data sets of MR images. Its performance is also compared with the previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed scheme over the existing approaches.     

Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising

Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper proposes a new image denoising algorithm based on low rank matrix restoration in order to solve this problem. The proposed algorithm introduces the non-local self-similarity error between the clear image and noisy image into the weighted Schatten p-norm minimization model using the non-local self-similarity of the image. In addition, the low rank error is constrained by using Schatten p-norm to obtain a better low rank matrix in order to improve the performance of the image denoising algorithm. The results demonstrate that, on the classic data set, when comparing with block matching 3D filtering (BM3D), weighted nuclear norm minimization (WNNM), weighted Schatten p-norm minimization (WSNM), and FFDNet, the proposed algorithm achieves a higher peak signal-to-noise ratio, better denoising effect, and visual effects with improved robustness and generalization.