A weighted mean filter with spatial-bias elimination for impulse noise removal

In this paper, we propose Unbiased Weighted Mean Filter (UWMF) for removing high-density impulse noise. Asymmetric distribution of corrupted pixels in the filtering window creates a spatial-bias towards the center of uncorrupted pixels. UWMF eliminates this bias by recalibrating the contribution factor (weight) of each uncorrupted pixel in such a way that the center shifts back to the center of the filtering window. The restoration process involves three sequential operations while convolving a filtering window over a contaminated image. Noise is detected, weights are recalibrated and the new intensity value is replaced by weighted mean using the recalibrated weights. Compared to the state-of-the-art impulse noise removal methods, UWMF provides superior performance, without requiring a fine-tuning for its parameters, in terms of both objective measurements and subjective assessments.     

A separable median filter for image noise smoothing

This paper investigates some properties of the separable filter resulting from successive applications of a one-dimensional median filter on the rows and columns of an image. Although the output of this separable filter is not identical to the corresponding nonseparable two-dimensional median filter with a square window, its performance in image noise smoothing is close. In particular, its effectiveness in smoothing noise and its behavior with edges are characterized and compared with those of the two-dimensional median filter. It is shown that the separable filter has a much simpler implementation in real-time hardware (at video rates, for example).     

椒盐噪声图像的非局部平均滤波算法

针对非局部平均(NLM)方法对椒盐噪声图像滤波效果较差的问题,通过引入噪声检测结果扩展NLM方法去除图像中椒盐噪声。在噪声检测阶段,利用图像的两个极值Lmin和Lmax把图像像素点分为非噪声点和噪声点。在滤波阶段,非噪声点的灰度值保持不变。对于噪声点,如果以该噪声点为中心的自适应滤波窗口内均为噪声点,则认为该噪声点位于图像自身灰度值为Lmin或Lmax的区域内,使用两个极值的统计结果进行恢复。否则,采用改进的NLM方法滤除噪声。构造联合噪声检测模板避免噪声点对相似权计算的干扰,噪声点的恢复值由非噪声点的灰度值加权平均得到。此外,采用迭代滤波策略对高密度噪声图像噪声点进行恢复。相关去噪实验结果证实了算法去噪的有效性,不足之处是算法的时间复杂度较高。     

A novel digital filter structure with minimum roundoff noise

In this paper, a novel structure is derived for efficient implementation of digital filters as well as minimizing the finite word length (FWL) errors. Such a structure is actually an improved version of that reported previously. The performance of this new structure and the famous normalized lattice structure are analyzed by deriving the corresponding expression for the roundoff noise gain. Design examples are presented to illustrate the behavior of the proposed structure and to compare it with some existing ones. It is shown that the proposed structure outperforms the others in terms of minimizing roundoff noise as well as implementation efficiency.     

有效的图像滤波算法

利用灰色关联度的特性和阿尔法均值滤波算法的优点,提出一种基于改进灰色关联度和阿尔法Alpha均值滤波的噪声图像的自适应滤波算法。该算法采用灰色关联度自适应地确定滤波窗口的加权系数值,改善算法的滤波性能。实验结果表明算法对受到高斯噪声和混合噪声干扰的图像进行去噪能取得较好的滤波效果,同时还保护了原始图像的细节信息。     

Adaptive noise smoothing filter for images with signal-dependent noise

In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.     

一种影像测量系统中自适应混合噪声滤波方法

介绍了影像测量系统中的噪声来源以及常用的滤波方法。针对均值滤波器和中值滤波器在滤除高斯噪声和脉冲噪声时各自表现出良好的性能,设计了一种针对待测零件图像中混合噪声的自适应滤波方法,系统根据噪声类别灵活选择滤波方法。实验证明改进的滤波策略能获得比传统滤波方法更高的信噪比改善因子。     

基于相似性噪声检测的边缘保护滤波算法

为了改善受脉冲噪声污染的图像的滤波效果,提出了一种新的滤波算法。该算法包括3个阶段,首先,利用像素点之间的相似性来检测图像中受噪声污染的像素点;然后,将滤波窗口分为8个主要方向来确定边缘方向;最后,针对噪声点进行边缘保护滤波。实验结果表明,在噪声污染度较小的情况下,该算法不仅能准确地检测出噪声点,而且更多地保护了噪声图像的边缘部分以及非噪声点,具有良好的滤波效果。     

A filter for a state space model with fractional Gaussian noise

A filter for estimating the hidden signal and parameters in a state space model, where the noise in the observations is fractional Gaussian noise, is obtained.     

含噪声驱动阵的相关噪声卡尔曼滤波及稳定性

探讨相关噪声下离散时变线性系统的卡尔曼滤波模型。借助广义逆和最小模最小二乘解的思想,在Frobenius范数意义下,获得基于偏差最优估计的转换系数矩阵,将相关噪声系统转化为不相关噪声系统,获得相应的卡尔曼滤波模型。理论上,在误差协方差矩阵有界前提下,获证该滤波模型是全局渐近稳定的,数值实验获该模型的合理性。理论和实验结果表明,该模型是稳定的,且可有效解决含相关噪声和时变量测噪声驱动阵的离散时变系统的状态估计问题。     

An adaptive noise smoothing filter for remotely-sensed images with microphonic noise

Abstract

In this paper we consider the restoration of airborne scanner images with microphonic noise. According to the practical generation process of micro-phonic noise, the noise can be assumed multiplicative and to be a stationary Markov random sequence. An adaptive noise smoothing filter, based on the Kalman filter and NMNV (non-stationary mean and non-stationary variance) image model, is developed. The microphonic noise in airborne scanner images can be effectively filtered out by this adaptive filter. Results of the algorithm on simulation and realistic images are shown.     

针对椒盐噪声的开关模糊滤波器

自适应中值滤波随着椒盐噪声密度增加滤波图像细节损失较大。在开关滤波和自适应中值滤波的基础上提出了开关模糊滤波（SF）。SF用Max-Min算子检测噪声点,然后根据滤波窗口中正常点数量以均值方法或者T-S模糊方法去噪。仿真实验表明,开关模糊滤波比自适应中值滤波能更好地保护边界和细节。开关模糊滤波在消除噪声和细节保护之间取得了良好的平衡。     

Edge-detection-based filter for SAR speckle noise reduction

Some adaptive filters, such as the Kuan, Lee, minimum mean square error (MMSE) and Frost filters, have been tested on synthetic aperture radar (SAR) data without considering the level of homogeneity in the pixels. Therefore, they degrade the spatial resolution of images and smooth details, while also decreasing the speckle noise level. There are other filters, such as the enhanced Lee and gamma maximum a posteriori (MAP), that utilize the level of homogeneity, but they cannot adequately suppress speckle noise. In addition to these weaknesses, pixels surrounding a point scatterer are also treated as point scatterers due to inadequacy of the method based on evaluating the coefficient of variation for differentiating between them and the point scatterer. We have developed a new method based on the assessment of similarity of homogeneity levels in the image, incorporating edge-detection filters to identify meaningful features and an algorithm to filter the pixels surrounding point scatterers. This method, called the UNSW (University of New South Wales) adaptive filter (UAF), was compared to nine filters using different quantitative and qualitative methods. The results show the ability of the UAF to simultaneously reduce speckle and preserve details as well as its ability to filter more pixels. The effect of increasing the damping factor on speckle noise reduction performance has also been assessed using this method.     

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.     

A filter for a hidden Markov chain observed in fractional Gaussian noise

Estimates of parameters and hidden states in a hidden Markov model, where the noise in the observations is fractional Gaussian noise, are obtained.     

A novel noise filter based on interesting pattern mining for bag-of-features images

Improving the quality of image data through noise filtering has gained more attention for a long time. To date, many studies have been devoted to filter the noise inside the image, while few of them focus on filtering the instance-level noise among normal images. In this paper, aiming at providing a noise filter for bag-of-features images, (1) we first propose to utilize the cosine interesting pattern to construct the noise filter; (2) then we prove that to filter noise only requires to mine the shortest cosine interesting patterns, which dramatically simplifies the mining process; (3) we present an in-breadth pruning technique to further speed up the mining process. Experimental results on two real-life image datasets demonstrate effectiveness and efficiency of our noise filtering method.     

用噪声检测算法改进理想低通滤波器

针对图像去噪算法存在滤除噪声与保留图像边缘细节之间的矛盾,提出了一种使用基于分数阶微分梯度的随机噪声检测算法来提高理想低通滤波器的去噪性能的方法。首先,使用不同方向的分数阶微分梯度模板与含噪声图像进行卷积,计算出图像在不同方向上的分数阶微分梯度;然后,依据预先设定的阈值获得不同方向的分数阶微分梯度检测图,将在所有选定方向上梯度都发生跳变的像素点判定为噪声点;最后,只对图像中被检测出的噪声点用理想低通滤波器进行滤波,可使图像在去除噪声和保留图像细节两方面同时获得较优的效果。实验结果表明,所提算法不仅可以获得更好的视觉效果,而且去噪后图像的峰值性噪比(PSNR)表明去噪后的图像更接近原始图像,使用理想低通滤波器获得的最大PSNR为29.0893dB,所提算法获得的最PSNR为34.7027dB。将分数阶微积分用于图像去噪,为提高图像去噪性能提供了一个新的研究方向。     

Optimization of PID controller with higher-order noise filter

The proposed PID controller optimization is based on the frequency response of a process G_p(s) and maximization of the proportional gain, under constraints on the desired sensitivity to measurement noise, desired maximum sensitivity and desired maximum complementary sensitivity. The set-point and load disturbance step responses with negligible overshoot are obtained for stable processes, processes with oscillatory dynamics, integrating and unstable processes. Simulations, with a band-limited white noise added to the controlled variable, and experimental results, on a laboratory thermal plant with noisy measurements, are used to demonstrate the effectiveness of the proposed PID optimization method.     

带有噪声递推估计的自适应集合卡尔曼滤波

卡尔曼滤波(KF)广泛应用于线性系统的状态估计问题.然而,它需要精确已知过程噪声的统计特性,这在实际应用中往往是不能满足的.在这个背景下,首先,根据协方差匹配原理建立一种带有过程噪声递推估计的自适应KF算法;然后,为了突破KF只能处理线性系统估计问题的局限,将过程噪声递推估计引入集合卡尔曼滤波(EnKF)中,提出一种自适应EnKF算法;最后,采用估计理论证明所提出算法的稳定性.与标准EnKF相比,该自适应算法在过程噪声统计特性未知的情况下滤波依然收敛,滤波精度及稳定性显著提升.仿真结果验证了所提出算法的有效性.