Partition-based fuzzy median filter based on adaptive resonance theory

This paper presents a novel partition-based fuzzy median filter for noise removal from corrupted digital images. The proposed filter is obtained as the weighted sum of the current pixel value and the output of the median filter, where the weight is set by using fuzzy rules concerning the state of the input signal sequence to indicate to what extent the pixel is considered to be noise. Based on the adaptive resonance theory, the authors developed a neural network model and created a new weight function where the neural network model is employed to partition the observation vector. In this framework, each observation vector is mapped to one of the M blocks that form the observation vector space. The least mean square (LMS) algorithm is applied to obtain the optimal weight for each block. Experiment results have confirmed the high performance of the proposed filter in efficiently removing impulsive noise and Gaussian noise.     

Partition belief median filter based on Dempster–Shafer theory for image processing

A novel median-type filter controlled by evidence fusion is proposed for removing noise from images. The fusion of evidence based on the Dempster–Shafer evidence theory, providing a way to deal with the uncertainty in the evidence fusion, indicates to what extent a noise is considered. The filter proposed here is obtained as a weighted sum of the current pixel value and the output of the median filter, and the weight is set based on the belief value of the input signal sequence. The efficient step-like function is used to partition the belief space, and the least mean square (LMS) algorithm is applied to obtain the optimal weight for each block. Moreover, to improve the performance, the new filter is recursively implemented. Experimental results have demonstrated that the proposed filter can outperform many well-accepted median-based filters in preserving image details while effectively suppressing impulsive noises, and it also works satisfactorily in reducing Gaussian as well as the mixture of Gaussian and impulsive noise.     

Adaptive two-pass median filter based on support vector machines for image restoration

In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.     

基于CIELAB空间的开关型矢量中值滤波器

矢量中值滤波器VMF（Vector median filter）是一种经典和高效的矢量滤波器,主要用于消除彩色图像中的脉冲噪声.然而VMF没有区分细线条和噪声的能力,往往把细线条当成噪声而过滤掉.本文先将彩色图像从RGB空间变换到均匀颜色空间CIELAB中,然后模仿Laplacian算子,提出一个用于检测彩色图像中的脉冲噪声的算法,并结合传统的VMF构造出一个新颖的开关型矢量中值滤波器.实验表明,新的滤波器不仅能有效地保护细线条和边界等细节信息,而且其滤波性能也明显胜过传统的VMF和一些经典的、及最近开发的矢量滤波器.     

基于反双曲正弦函数的抗冲激块稀疏自适应滤波算法

针对现有基于最小均方误差（MSE）的块稀疏系统辨识算法抗冲激性能不佳的问题,提出了一种利用反双曲正弦函数替代最小均方误差的改进型块稀疏归一化最小均方（IBS-NLMS）算法。该算法首先构造新的代价函数,利用负梯度最陡下降法求出增量,进而导出了新的滤波器权系数更新公式,在公式迭代过程中出现的冲激噪声会导致权系数的更新量趋于零向量,从而消除了由于非高斯冲激干扰而导致的算法发散问题。同时,理论分析并推导出了该算法的均值收敛过程。块稀疏系统辨识的仿真结果表明,在非高斯冲激噪声干扰和截断变化情况下,改进型算法与块稀疏归一化最小均方（BS-NLMS）算法相比有更快的收敛速度和更小的稳态误差。     

An extended packetization-aware mapping algorithm for scalable video coding in finite-length fountain codes

We propose an extended packetization-aware mapping algorithm based on fountain codes to enhance video stream performance which is vulnerable to packet losses.By properly utilizing the proposed algorithm in finite-length cases,the edges connecting to a source symbol are scattered over multiple encoding packets so that the decoding probabilities are increased.Furthermore,an improved degree distribution is designed to obtain better decoding probabilities.Numerical results,it is confirmed that the proposed algorithm in finite-length cases can augment decoding probabilities and the improved degree distribution can increase peak signal-to-noise ratio(PSNR) of scalable video coding(SVC) in a hostile communication environment.     

基于四元数旋转的开关型矢量中值滤波器

矢量中值滤波器是一种经典和高效的矢量滤波器,主要用于消除彩色图像中的冲击噪声。然而VMF没有区分细线条和噪声的能力,它往往把细线条当成噪声而过滤掉。本文利用四元数旋转理论,模仿Laplacian算子,提出一种用于检测彩色图像中的冲击噪声的算法,并结合传统的VMF构造出一个新颖的开关型矢量中值滤波器。实验结果表明,新的滤波器不仅能有效地保护细线条和边界等细节信息,而且其滤波性能也明显胜过传统的VMF和一些经典的及最近开发的矢量滤波器。     

Pipelined robust M-estimate adaptive second-order Volterra filter against impulsive noise

The main limits on adaptive Volterra filters are their computational complexity in practical implementation and significant performance degradation under the impulsive noise environment. In this paper, a low-complexity pipelined robust M-estimate second-order Volterra (PRMSOV) filter is proposed to reduce the computational burdens of the Volterra filter and enhance the robustness against impulsive noise. The PRMSOV filter consists of a number of extended second-order Volterra (SOV) modules without feedback input cascaded in a chained form. To apply to the modular architecture, the modified normalized least mean M-estimate (NLMM) algorithms are derived to suppress the effect of impulsive noise on the nonlinear and linear combiner subsections, respectively. Since the SOV-NLMM modules in the PRMSOV can operate simultaneously in a pipelined parallelism fashion, they can give a significant improvement of computational efficiency and robustness against impulsive noise. The stability and convergence on nonlinear and linear combiner subsections are also analyzed with the contaminated Gaussian (CG) noise model. Simulations on nonlinear system identification and speech prediction show the proposed PRMSOV filter has better performance than the conventional SOV filter and joint process pipelined SOV (JPPSOV) filter under impulsive noise environment. The initial convergence, steady-state error, robustness and computational complexity are also better than the SOV and JPPSOV filters.     

等级阈值的彩色图像矢量中值滤波

为消除彩色图像中的脉冲噪声,提出一种新的基于等级阈值的矢量中值滤波器。该滤波器设计了一组随滤波窗口内当前像素排序位置而等级变化的阈值,并根据当前像素与经典矢量滤波器输出的距离差值来判断噪声像素的存在。仿真实验证明,该方法较其他非开关型和开关型矢量中值滤波器能更好保存原图像细节和消除脉冲噪声。     

A novel adaptive SVR based filter ASBF for image restoration

In this paper, a novel adaptive filter ASBF based on support vector regression (SVR) is proposed to preserve more image details and efficiently suppress impulse noise simultaneously. The main idea of the novel filter ASBF here is to employ a SVR based impulse detector to judge whether an input pixel is contaminated or not by impulse noise. If this case happens, a median filter is employed to remove the corresponding impulse noise. This judgment procedure is executed by regressing the filter window of an input pixel using SVR and then judging the input pixel by its regression distance. Huber loss function is used in SVR regression, due to its excellent robustness capability. The distinctive advantage of the filter ASBF over the latest Support Vector Classifier (SVC) based filter is that no training for the original noise-free image is required in our approach, which is well in accordance with our visual judgment way. Experimental results for benchmark images demonstrate that our filter ASBF here outperforms the extensively-used median-based filters and the SVC based filter.     

Adaptive Unsymmetrical Trim-Based Morphological Filter for High-Density Impulse Noise Removal

The modified decision-based unsymmetrical trimmed median filter (MDBUTMF), which is an efficient tool for restoring images corrupted with high-density impulse noise, is only effective for certain types of images. This is because the size of the selected window is fixed and some of the center pixels are replaced by a mean value of pixels in the window. To address these issues, this paper proposes an adaptive unsymmetrical trim-based morphological filter. Firstly, a strict extremum estimation approach is used, in order to decide whether the pixel to be processed belongs to a monochrome or non-monochrome area. Then, the center pixel is replaced by a median value of pixels in a window for the monochrome area. Secondly, a relaxed extremum estimation approach is used to control the size of structuring elements. Then an adaptive structuring element is obtained and the center pixel is replaced by the output of constrained morphological operators, i.e., the minimum or maximum of pixels in a trimmed structuring element. Our experimental results show that the proposed filter is more robust and practical than the MDBUTMF. Moreover, the proposed filter provides a preferable performance compared to the existing median filters and vector median filters for high-density impulse noise removal.

     

基于噪声检测的彩色图象脉冲噪声滤波

文章提出了具有细节保持能力的自适应彩色图像脉冲噪声滤波器,称为细节保持滤波器。新方法对图像中噪声像素进行检测,仅对噪声像素进行有序滤波而对非噪声像素则保持其原值不变,并根据图像噪声情况自适应地选择滤波窗口。从而,有效地滤除随机彩色脉冲噪声、保持图像边缘与细节,其性能优于经典的矢量中值滤波器（ＶＭＦ）、方向一距离滤波器（ＤＤＦ）、距离一幅度矢量滤波器（ＤＭＶＦ）等非线性滤波器。     

Computational noise effects on adaptive filter algorithms

Finite word length arithmetic roundoff noise in adaptive filter algorithms results in statistical variations in the filter weight vector about the infinite precision arithmetic weight vector. These roundoff errors may be modeled as a statistically non stationary driving noise affecting weight mean and covariance convergence. Mean and covariance expressions and bounds are desired for word lengths in fixed-point arithmetic by making use of multiplication roundoff error models. The adaptive filter algorithms consist of the LMS algorithm, the Widrow-Hoff LMS algorithm, pilot-vector algorithm and clipped vector algorithm. All of these algorithms can be implemented on-line and real-time. However, only the behavior of the LMS algorithm is reported here. The implementation of the adaptive filter algorithms in finite word length arithmetic is most evident in minicomputer, microprocessor, and dedicated digital signal processors for on-line real-time signal identification and parameter estimation in many disciplines. Radar signal processing, adaptive beam forming, acoustic signal identification, communication channel enhancement have a definite need for advanced filtering concepts. Our adaptive algorithms are typically employed in these filter configurations. These filters can also be employed in phase distortion equalizers. A particular advantage of these filters is that they can be trained to equalize a variety of distortions. Should a particular distortion scenario change in time, the filters can be made to easily adapt to the new problem.     

扩散式变阶数最大相关熵准则算法

固定阶数的分布式自适应滤波算法只有在待估计向量的阶数已知且恒定的情况下才能达到相应的估计精度,在阶数未知或时变的情况下算法的收敛性能会受到影响,变阶数的分布式自适应滤波算法是解决上述问题的有效途径。但是目前大多数分布式变阶数自适应滤波算法以最小均方误差(Mean square Error, MSE)准则作为滤波器阶数的代价函数,在脉冲噪声环境下算法的收敛过程会受到较大影响。最大相关熵准则具有对脉冲噪声的强鲁棒性,且计算复杂度低。为提高分布式变阶数自适应滤波算法在脉冲噪声环境下的估计精度,利用最大相关熵准则作为滤波器阶数迭代的代价函数,并将得到的结果代入固定阶数的扩散式最大相关熵准则算法,提出了一种扩散式变阶数最大相关熵准则(Diffusion Variable Tap-length Maximum Correntropy Criterion, DVTMCC)算法。通过与邻域的节点进行通信,所提算法以扩散的方式实现了整个网络的信息融合,具有估计精度高、计算量小等优点。仿真实验对比了在脉冲噪声下DVTMCC算法和其他分布式变阶数自适应滤波算法、固定阶数的扩散式最大相关熵准则算法的收敛性能。...     

Efficient algorithms of clustering adaptive nonlinear filters

This paper proposes a new class of efficient adaptive nonlinear filters whose estimation error performance (in a minimum mean square sense) is superior to that of competing approximate nonlinear filters, e.g., the well-known extended Kalman filter (EKF). The proposed filters include as special cases both the EKF and previously proposed partitioning filters. The new methodology performs an adaptive selection of appropriate reference points for linearization from an ensemble of generated trajectories that have been processed and clustered accordingly to span the whole state space of the desired signal. Through a series of simulation examples, the approach is shown significantly superior to the classical EKF with comparable computational burden     

一种改进的n维空间递归滤波算法

基于递归运算准则,本文提出了一种n维空间非线性滤波器的改进算法。该算法利用像素点的周边信息完成对噪声点的识别与修复。此n维空间滤波器可以递归地分解到更低一维空间,本文主要分析了这种改进的滤波算法在二维空间信号消噪处理中的性能。仿真结果表明,与中值滤波和Peak-and-Valley滤波算法比较,该算法在信噪比和图像细节保留方面具有更大的优势,并且在有高强度的脉冲噪声时也能达到较为理想的滤波效果。     

一种改进的自适应中值滤波算法

针对未知脉冲噪声强度的退化图像的去噪,提出了一种新的自适应中值滤波算法,该算法主要基于以下两点：（1）根据模糊数学里的模糊度理论及随机脉冲噪声本身的去噪特点,提出了模糊指标的概念,并通过反向二阶拟合来获得噪声的强度信息;（2）引入了反映图像边缘信息的Prewitt梯度算子,并通过实验来得到合适的梯度阚值,以更好地保持图像的边缘等细节信息.通过将该算法与传统的中值滤波、基于排序阈值的开关中值滤波以及Sorin Zoican提出的改进的中值滤波进行的对比实验表明,该算法对噪声的强度有很好的估计,不仅提高了噪声去除的自适应性,尤其对含噪声多的图像的处理效果更为理想.     

An RBFN–Wiener hybrid filter using higher order signal statistics

In this paper we propose a radial basis function network (RBFN) based nonlinear filter with a basic framework of a linear Wiener filter. In addition, in order to improve the filtering performance, we further propose a novel nonlinear filter, which is synthesized by a hybridization of an RBFN filter and a linear Wiener filter. The proposed filters are realized with a least mean square error scheme using higher order statistics of a target signal and an observation noise.The validity and the effectiveness of the proposed filters have been verified by applying them to the actual filtering problems of the noisy images.     

模糊加权中值滤波器

本文介绍了一种模糊加权中值滤波器,该滤波器由模糊布尔函数和滤波加权确定。本文用S型函数逼近模糊布尔函数。此外,用模糊理论领域中使用的S型函数逼近所滤波的加权。模糊加权中值滤波器只由4个参数确定。所提出的滤波在均方误差准则下能够由最小均方算法导出。图像复原的实验结果表明,本文介绍的模糊加权中值滤波方法既能去除脉冲噪声和平滑高斯噪声,又能同时有效地保持边缘和图像细节,漠糊加权中值滤波器明显优于加权中值滤波器,也优于Wiener滤波器。     

基于曲率差分的自适应全变分去噪算法

针对整体变分（TV）修复模型易受到梯度的影响而且常常会丢失图像细节信息的缺点,提出了一种基于曲率差分的自适应全变分去噪算法。在联合非线性各向异性扩散滤波器和冲击滤波器对含噪图像做预处理的基础上,通过自适应方式调节正则项和保真项的权重系数,该算法能同时兼顾边缘保留和图像平滑去噪。仿真实验结果表明：与现有的去噪算法相比,该算法在不同强度的脉冲噪声下可以将峰值信噪比提升14%以上,同时将归一均方误差降低43%以上。