Scale selection for anisotropic diffusion filter by Markov random field model

The selection of stopping time (i.e., scale) significantly affects the performance of anisotropic diffusion filter for image denoising. This paper designs a Markov random field (MRF) scale selection model, which selects scales for image segments, then the denoised image is the composition of segments at their optimal scales in the scale space. Firstly, statistics-based scale selection criteria are proposed for image segments. Then we design a scale selection energy function in the MRF framework by considering the scale coherence between neighboring segments. A segment-based noise estimation algorithm is also developed to estimate the noise statistics efficiently. Experiments show that the performance of MRF scale selection model is much better than the previous global scale selection schemes. Combined with this scale selection model, the anisotropic diffusion filter is comparable to or even outperform the state-of-the-art denoising methods in performance.     

Speckle reduction of ultrasound images based on Rayleigh-trimmed anisotropic diffusion filter

A novel method is proposed to reduce speckle in ultrasound images. Based on the assumption of Rayleigh distribution of speckle, a Rayleigh-trimmed filter is first proposed to estimate the relative standard deviations of local signals and the results are used to determine the parameter that controls an alpha-trimmed mean filter for suppressing the primary noise. Then the anisotropic diffusion is subsequently applied to further reduce noise while enhancing features and structures in the original image. We also extend the proposed method to three-dimensional space by introducing time as one additional dimension. The proposed method effectively utilizes the statistical characteristics of speckle and the two-step despeckling algorithm reduces speckle significantly while retaining important features. The effectiveness of the proposed method is well demonstrated by experiments on both simulated and real ultrasound images.     

The solutions of equation-based noise detector for an adaptive median filter

Techniques of noise detection have been widely applied in impulse noise reduction. However, the phenomenon of pixel misclassification is very obvious in high noise density. In order to improve pixel identification, in this paper, the new noise detector is proposed. Based on solutions of equations, an estimated block of every 8×8 block of a noise image is generated. Then, according to relationships between these noise blocks and their estimated blocks, corrupted and uncorrupted pixels are identified. During image filtering, a noise-detection-based adaptive median algorithm is presented. Experimental results show that the proposed filter can well reduce the impulse noise and preserve more details of original images.     

椒盐图像的方向加权均值滤波算法

椒盐噪声是造成图像污染的主要因素之一,椒盐去噪是图像去噪领域的研究热点。方向加权中值滤波算法计算噪声点滤波输出时存在一定的问题,比如,未排除近邻噪声点的干扰,对方向的估计不准确,对局部灰度特性刻画不完整等。为此,提出一种方向加权均值滤波算法。此算法先根据方向灰度差异和灰度极值判断检测噪声点,然后根据对局部窗口噪声强度的估计自适应地选择递归或非递归滤波窗口的加权灰度均值作为滤波输出。仿真实验结果表明,提出的算法与现有的两种方向加权中值滤波算法相比,PSNR普遍提高了2~3dB和5~6dB,噪声密度高时提高的幅度更加明显;速度提高了接近10倍和30倍。     

现代数字图像噪声滤除技术及其评价

图像噪声滤除是数字图像处理领域中一项重要的方法技术。首先在对图像噪声模型及其特征介绍的基础上,对去除图像噪声的传统线性滤波和非线性滤波技术,以及针对脉冲噪声、高斯噪声等的新颖滤波理论技术进行了分析与综述,最后对图像噪声滤除的质量评价、现状与发展做了探讨与展望。     

Rapid and efficient image restoration technique based on new adaptive anisotropic diffusion function

The anisotropic diffusion is an efficient smoothing process. It is widely used in noise removing and edges preserving via different schemes. In this paper based on a mathematical background and the existing efficient anisotropic function in the literature we developed a new mathematical anisotropic diffusion function which is able to overcome the drawbacks of the traditional process such as the details loss and the image blur. The simulations results and the comparative study with other recent techniques are conducted and showed that the proposed schema generates a wide improvement in the quality of the restored images. This improvement has been shown subjectively in terms of visual quality, and objectively with reference to the computation of some criteria. The simulated images are well de-noised but the most important is that details and structural information are kept intact. In addition to that, the proposed new function was found very interesting and presents numerous advantages like its similarity to the conventional model and the importance of the speed hence it converges faster which allows an opportunity to be well implemented in our de-noising process.     

小波域上分形噪声的一种自适应Wiener滤波方法

针对淹没在1/f分形噪声中的有用信号恢复问题,提出了一种基于小波变换与Wiener滤波的多尺度自适应滤波算法。首先将带有1/f分形噪声的信号分解成多尺度的子带信号,通过小波变换对1/f分形噪声的白化作用,消除了1/f分形噪声的自相似性和长程相关性。然后在小波域内,利用自适应Wiener滤波实现了噪声和有用信号的分离,估计出了各子带中的有用信号。最后进行小波重构,较好地恢复出淹没在1/f分形噪声中的有用信号。仿真实验表明,使用多尺度自适应Wiener滤波器能有效地抑制分形噪声,显著地提高信噪比。     

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.     

Color filter array design using random patterns with blue noise chromatic spectra

We propose two new types of random patterns with R, G, B colors, which allow to design color filter arrays (CFAs) with good spectral properties. Indeed, the chrominance channels have blue noise characteristics, a property which maximizes the robustness of the acquisition system to aliasing. With these new CFAs, the demosaicking artifacts appear as incoherent noise, which is less visually disturbing than the moiré structures characteristic of CFAs with periodic patterns.     

一种快速自适应数字图像脉冲噪声滤除算法

根据脉冲噪声的特点,利用检测窗口内像素灰度值的统计信息,自适应地将数字图像中的噪声点检测出来,滤波算法只对噪声点进行处理,用噪声点邻域内所有信号点去极值后的平均值作为噪声点的滤波输出,实验结果表明该算法的滤波性能和计算速度都明显好于常用的中值滤波,具有良好的实用价值.     

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。     

On the impact of anisotropic diffusion on edge detection

Content-aware, edge-preserving smoothing techniques have gained visibility in recent years. However, they have had a rather limited impact on the edge detection literature compared to content-unaware (linear) techniques, often based on Gaussian filters. In this work, we focus on Anisotropic Diffusion, covering its initial definition by Perona and Malik and subsequent extensions. A visual case study is used to illustrate their features. We perform a quantitative evaluation of the performance of the Canny method for edge detection when substituting linear Gaussian smoothing filters by Anisotropic Diffusion.     

Disturbance attenuation for Markov jump linear system over an additive white Gaussian noise channel

This paper studies the stabilisability and the performance of stochastic disturbance attenuation of a Markov jump linear system whose feedback channel is subject to an additive white Gaussian noise. First an inequality of differential entropy of random vectors under Markov switching is presented. Then by the concept of entropy power and the theory of information, a necessary condition to stabilise the system is obtained. This requires that the signal-to-noise ratio in the feedback channel is bigger than a specified value. Furthermore, to evaluate the performance of disturbance attenuation, a lower bound of the maximum fluctuation of the system state is presented.     

种子像素滤波法去除随机脉冲噪声

为了在去除图像高密度随机脉冲噪声的同时最大程度地保护图像边缘和细节,提出一种新方法。该方法首先利用图像局部灰度相似性,提取种子像素;然后只对种子像素进行开关中值滤波,去除误判的种子像素,采用自适应变窗口尺寸;最后利用种子像素先探测漏选的种子像素,接着重构非种子像素。实验结果表明,与其他方法相比,该方法是综合性能最优之一。     

Simulation and modeling of hydro acoustic communication channels with wide band attenuation and ambient noise

Acoustic waves are the preferred medium for long-range underwater communications. Increasing number of innovative methods for underwater communication using acoustic waves appears. Performance of underwater communications for the new methods needs to be evaluated through simulation. Simulation of underwater acoustic communications is challenging due to many impediments, including attenuation, multipath propagation, noise and Doppler spread. In this paper, a baseline time domain simulation model is extended to several frequency-domain models. The proposed frequency models, including two incoherent models and a coherent model, take multipath attenuation and ambient noise into account. An incoherent linear fitting model and a coherent model are simulated and compared with a theoretical reference and the baseline time model. The proposed incoherent models are also compared with one another. Simulation shows that the incoherent linear fitting model produces results similar to the multi-frequency-merge-path model, but requires less computation time. In addition, the proposed coherent model is compared with field experimental data. The coherent model with color noise, in the frequency domain, can match closely the bit error rates of the field experimental data.     

基于均值操作的快速自适应滤波器

为了满足图象实时处理对算法速度和高斯噪声,脉冲噪声混合的噪声环境对算法鲁棒性的要求,以及适应能够同时抑制高斯噪声和脉冲噪声的需要,提出了一种可以有效滤除混合噪声（高斯噪声和正负脉冲噪声）,而且可以快速实现的自适应滤波器－－ＡＢＡ滤波器,ＡＢＡ滤波器,ＡＢＡ滤波器应用了自适应的滤波结构,它将以脉冲噪的结果充分利用在自适应处理中,实验仿真所得的数据显示,在脉冲噪声的密度小于１０％的情况下,与其它一些滤     

一类四阶各向异性扩散方程在图像放大中的应用

在文中我们首先分析了进行图像放大时各向异性偏微分方程优于各向同性偏微分方程,随后我们分析了在本文中不同四阶模型的扩散方向.为了消除低阶偏微分方程在处理图像中出现的块状效应的影响,同时保证方程为各向异性扩散,我们构造了两个各向异性的四阶偏微分方程,并且分别从数据和放大图像效果两方面来说明我们给出的模型优于文中提到的其它四个模型.     

带噪声统计估计器的Unscented卡尔曼滤波器设计

针对传统Unscented卡尔曼滤波器(UKF)在噪声先验统计未知或不准确时滤波精度下降甚至发散的问题,基于极大后验(MAP)估计原理,设计了一种带噪声统计估计器的UKF.该UKF滤波算法在进行状态估计的同时,能实时估计和修正噪声均值和协方差.相比于传统UKF,所提出的UKF具有应对噪声统计变化的自适应能力.仿真结果表明了该UKF滤波算法的有效性.

Abstract：

For the problem that the accuray of the conventional UKF declines and further diverges when the prior noise statistic is unknown or inaccurate, an unscented Kalman filter (UKF) with noise statistic estimator is designed.This UKF filtering algorithm based on maximum a posterior (MAP) estimation can estimate and correct the mean and covariance of the noise in real time while it estimates the states.The proposed UKF has the adaptive capability of dealing with variable noise statistic.The simulation results show the effectiveness of the proposed UKF filtering algorithm.     

INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control

In classification, noise may deteriorate the system performance and increase the complexity of the models built. In order to mitigate its consequences, several approaches have been proposed in the literature. Among them, noise filtering, which removes noisy examples from the training data, is one of the most used techniques. This paper proposes a new noise filtering method that combines several filtering strategies in order to increase the accuracy of the classification algorithms used after the filtering process. The filtering is based on the fusion of the predictions of several classifiers used to detect the presence of noise. We translate the idea behind multiple classifier systems, where the information gathered from different models is combined, to noise filtering. In this way, we consider the combination of classifiers instead of using only one to detect noise. Additionally, the proposed method follows an iterative noise filtering scheme that allows us to avoid the usage of detected noisy examples in each new iteration of the filtering process. Finally, we introduce a noisy score to control the filtering sensitivity, in such a way that the amount of noisy examples removed in each iteration can be adapted to the necessities of the practitioner. The first two strategies (use of multiple classifiers and iterative filtering) are used to improve the filtering accuracy, whereas the last one (the noisy score) controls the level of conservation of the filter removing potentially noisy examples. The validity of the proposed method is studied in an exhaustive experimental study. We compare the new filtering method against several state-of-the-art methods to deal with datasets with class noise and study their efficacy in three classifiers with different sensitivity to noise.     

Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor

A unified multi-sensor optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense. The criterion considers the correlation among local estimation errors, only requires the computation of scalar weights, and avoids the computation of matrix weights so that the computational burden can obviously be reduced. Based on this fusion criterion and Kalman predictor, an optimal information fusion filter for the input white noise, which can be applied to seismic data processing in oil exploration, is given for discrete time-varying linear stochastic control systems measured by multiple sensors with correlated noises. It has a two-layer fusion structure. The first fusion layer has a netted parallel structure to determine the first-step prediction error cross-covariance for the state and the filtering error cross-covariance for the input white noise between any two sensors at each time step. The second fusion layer is the fusion center to determine the optimal scalar weights and obtain the optimal fusion filter for the input white noise. Two simulation examples for Bernoulli-Gaussian white noise filter show the effectiveness.