基于噪声检测的总变分去噪算法

对受高斯和脉冲混合噪声污染的数字图像去噪方法进行了研究,提出了一种基于噪声检测的自适应总变分(TV)去噪算法。提出的改进算法采用两步迭代框架实现:脉冲噪点检测和全变分图像恢复。第一步中,考虑到脉冲噪声污染的像素点不包含原图像有效信息,采用一种局部统计值,即邻域像素间的随机绝对差排序值(ROAD)估计出噪点的位置;第二步中,采用L2-TV方法进行去噪处理,并对上述过程进行迭代处理,得到去噪图像。在噪点估计过程中引入脉冲噪点水平参数,这样处理的优势在于可更准确地检测出脉冲噪点;而L2-TV去噪方法可很好地去除高斯噪声,两者结合有效地解决了TV算法存在误判图像脉冲噪声为边缘而产生假边缘的问题。与现有典型去噪方法的比较实验表明,该迭代去噪算法,即TV-ROAD算法,既能够去除混合噪声,又可以保留图像细节特征。     

去除图像中高斯-脉冲噪声的有效方法

数字图像在获取和传输过程中常常会受到噪声的污染,有时会同时受到高斯噪声和脉冲噪声的污染。然而现有的去噪算法大多针对单一的高斯噪声或脉冲噪声,在处理混合噪声时无法取得令人满意的去噪效果。给出了一种去除数字图像中高斯-脉冲混合噪声的有效方法,去噪过程分为两个步骤：首先采用一种称为边界判定噪声检测的脉冲噪声检测方法检测出混合噪声中的脉冲噪声,对噪声图像作中值滤波后得到一幅受高斯噪声污染的过渡图像。然后用贝叶斯最小平方-高斯尺度混合模型对过渡图像进行滤波得到降噪后的图像。实验表明,同现有的其他去噪方法相比,该方法能够更有效地去除混合噪声。     

图像去噪混合滤波方法

传统均值滤波和中值滤波对高斯型噪声和椒盐型噪声有着不同的滤波特性。实际滤波时，由于图像往往会受到两种不同性质噪声的同时干扰，因此，单独采用中值滤波或均值滤波都不会达到最好的去噪效果，为了能同时对两种不同性质的噪声进行滤除，现提出了一种新的混合滤波算法，该算法首先利用局部阈值把受高斯型噪声污染的像素和受脉冲型噪声污染的像素区别开来，然后对受高斯噪声污染的像素采用均值滤波算法，而对受椒盐噪声污染的像素则采用中值滤波算法进行去噪。仿真结果证明，该方法更具有实用性和有效性。     

Image Filter for Removal of Impulse Noise Based on Directional Derivatives

One of the most common image processing tasks involves the removal of noise from images. Noise can be introduced during image capture, during transmission, or during storage. For design purposes, noise sources are frequently approximated by random variables with a known probability distribution. One common noise model corrupts a signal by introducing impulses. And the surface of the image disturbed by impulse noise displays many peaks or vales. According to the characteristic of impulse noise, a novel algor...     

Sparse regularization method for the detection and removal of random-valued impulse noise

In this paper, we propose a novel two-stage algorithm for the detection and removal of random-valued impulse noise using sparse representations. The main aim of the paper is to demonstrate the strength of image inpainting technique for the reconstruction of images corrupted by random-valued impulse noise at high noise densities. First, impulse locations are detected by applying the combination of sparse denoising and thresholding, based on sparse and overcomplete representations of the corrupted image. This stage optimally selects threshold values so that the sum of the number of false alarms and missed detections obtained at a particular noise level is the minimum. In the second stage, impulses, detected in the first stage, are considered as the missing pixels or holes and subsequently these holes are filled-up using an image inpainting method. Extensive simulation results on standard gray scale images show that the proposed method successfully removes random-valued impulse noise with better preservation of edges and other details compared to the existing techniques at high noise ratios.     

一种改进的脉冲噪声滤波方法

由于数字图像在生成与传输过程中容易受到脉冲噪声的污染,往往造成后续处理难以为继。为了改善图像质量,需要对图像进行去噪处理。针对传统中值滤波及其它非线性滤波方法在去除图像脉冲噪声时存在的不足,本文提出了一种改进的去噪方法:在滤波之前进行一次脉冲噪声检测,确定受到噪声污染的像素点,并进行记录标识;然后根据检测结果进行改进的中值滤波:只对判断为噪声点的像素进行处理,不仅考虑了标准中值,也分情况利用了中值的前一个值和中值的后一个值的信息。实验表明,改进方法不仅在滤除脉冲噪声方面相比其他非线性滤波有很大改进,而且它可以更好地保护图像的细节特性,对图像的后续处理有很好的价值。     

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ROLD统计量的混合噪音线性滤波算法

提出一种基于ROLD统计量的混合噪音线性滤波算法（RLMF）。算法把用来检测脉冲噪音的ROLD统计量运用于混合噪音的滤波算法上,提高了混合噪音中脉冲噪音成分的检测效率,它不仅适用于恢复被混合噪音污染的数字图像,而且也适用于恢复被纯脉冲噪音或纯高斯噪音污染的数字图像。仿真实验证明,RLMF滤波后的图像视觉效果和PSNR均优于已知的同类滤波器。     

Fast Two-Phase Image Deblurring Under Impulse Noise

In this paper, we propose a two-phase approach to restore images corrupted by blur and impulse noise. In the first phase, we identify the outlier candidates—the pixels that are likely to be corrupted by impulse noise. We consider that the remaining data pixels are essentially free of outliers. Then in the second phase, the image is deblurred and denoised simultaneously by a variational method by using the essentially outlier-free data. The experiments show several dB’s improvement in PSNR with respect to the typical variational methods.     

Data mining based noise diagnosis and fuzzy filter design for image processing

In image processing, both diagnosis of noise types and filter design are critical. Conventional filtering techniques for image restoration such as median filter and mean filter are not effective in many cases, such as the case lacking the information of noise types or the case having mixed noise in images. This paper develops a data mining approach for noise type diagnosis, and proposes a fuzzy filter design for enhancing the quality of noise corrupted images. The experimental results demonstrate that the proposed technique outperforms the conventional filters, particularly for dealing with the images corrupted by mixed noise with additive Gaussian noise and impulse noise.     

Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images

This paper proposes a new median filter using prior information to capture natural pixels for restoration. In addition to being very efficient in logic execution, the proposed filter restores corrupted images with 1–99% levels of salt-and-pepper impulse noise to satisfactory ones. Without any iteration for noise detection, it intuitively and simply recognizes impulse noises, while keeping the others intact as nonnoises. Depending on different noise ratios at an image, two different sets of masked pixels are employed separately for the adoption of candidates for median finding. Furthermore, no limit to the size of mask windows assures that a proper median can be found. The simple logic of the proposed algorithm achieves significant milestones on the fidelity of a restored image. Moreover, the very fast execution speed of the proposed filter is very suitable for being applied to real-time processing. Relevant experimental results on subjective visualization and objective digital measure are reported to validate the robustness of the proposed filter.     

一种新的基于PCNN的图像脉冲噪声滤波算法

为了更好地滤除图像中的脉冲噪声,在分析脉冲耦合神经网络(PCNN)的工作机理的基础上,在PCNN中引入了点火映射图的概念,并提出了一种新的基于PCNN点火映射图的图像脉冲噪声滤波器。该方法首先通过运行PCNN把噪声图像转化为点火映射图,然后利用点火映射图对图像中的脉冲噪声进行定位,最后仅对定位的噪声进行自适应滤波。计算机仿真实验表明,该方法的去噪效果优于传统方法,从而验证了该方法的有效性、合理性。     

基于中值的自适应均值滤波算法

中值滤波和均值滤波通常被分别用来处理脉冲噪声和高斯噪声.但是当图像同时存在高斯噪声和脉冲噪声时,单独用那种滤波方法都不会达到最好的去噪效果.针对该问题,提出了一种基于中值的改进自适应加权均值滤波算法.该算法采用了一种基于自适应阈值的方法对滤波系数加以优化,使其可以更有效地减少噪声,又可以较好的保持图像的边缘细节信息.实验结果表明,该算法能较好的滤除混合噪声,且滤波效果优于传统的滤波算法.     

一种针对彩色图像随机值脉冲噪声的矢量滤波方法

为了去除彩色图像随机值脉冲噪声,提出了一种新的矢量滤波方法。该方法对图像的平滑区域和边缘区域的滤波工作分开进行,平滑区域滤波方法将窗口分成多个区域,然后基于矢量中值和平滑区域像素的特征检测出平滑区域的信号,边缘区域的滤波是在已知信号的基础上对非信号进行矢量中值滤波。仿真实验结果表明,该方法能够有效地去除彩色图像的随机值脉冲噪声,尤其当噪声密度较高时,去噪效果明显优于传统的矢量中值滤波。     

Image denoising using 2-D FIR filters designed with DEPSO

Digital images are often corrupted by additive noises during transmission. Thus, how to alleviate noise as much as possible has received concerns for decades. In this paper, we present a simple denoising method based on two dimensional (2-D) finite impulse response (FIR) filtering, where by differential evolution particle swarm optimization (DEPSO) algorithm, five two dimensional finite impulse response filters are designed to filter different kinds of pixels. Comprised by differential evolution algorithm and particle swarm optimization algorithm, differential evolution particle swarm optimization algorithm is effective and robust, which helps to yield better denoise performance. And computer simulation demonstrates that the proposed method is superior to the conventional lowpass filtering method, as well as the modern bilateral filtering and stochastic denoising method.     

基于检测的脉冲噪声滤除方法

为了将柴油机瞬时转速信号中的脉冲噪声滤除,使用了基于检测的脉冲噪声滤除方法。在已有的检测方法基础上,计算被检测点与中值之差将已有的检测方法可能漏掉的脉冲噪声检测出来,改进了脉冲噪声的检测方法来保护信号的细节信息。而且此方法对脉冲噪声干扰较为严重的信号特别有效。使用本方法对含有大量脉冲噪声的柴油机瞬时转速信号的滤波结果表明,改进的脉冲噪声检测法能够更准确地检测出脉冲噪声,更好地保护信号中的细节信息。     

Wavelet-Based Identification of Linear Discrete-Time Systems: Robustness Issue

Discrete-time linear time-varying systems are modeled by discrete-time wavelets. The output of the unknown system is corrupted by noise. The system model parameters are estimated by the least-squares method applied to the output error. Conditions are derived that provide vanishing influence of the output noise to the parameter estimates. Due to the time-frequency selectivity of wavelets, parameter estimates can be robust to narrow-band noise and/or impulse noise. This robustness is confirmed by simulations.     

An automatic filtering convergence method for iterative impulse noise filters based on PSNR checking and filtered pixels detection

Whether input images are corrupted by impulse noise and what the noise density level is are unknown a priori, and thus published iterative impulse noise filters cannot adaptively reduce noise, resulting in a smoothing image or unclear de-noising. For this reason, this paper proposes an automatic filtering convergence method using PSNR checking and filtered pixel detection for iterative impulse noise filters. (1) First, the similarity between the input image and the 1st filtered image is determined by calculating MSE. If MSE is equal to 0, then the input image is unfiltered and becomes the output. (2) Otherwise, one applies PSNR checking and filtered pixel detection to estimate the difference between the tth filtered image and the t–1th filtered image. (3) Finally, an adaptive and reasonable threshold is defined to make the iterative impulse noise filters stop automatically for most image details preservation in finite steps. Experimental results show that iterative impulse noise filters with the proposed automatic filtering convergence method can remove much of the impulse noise and effectively maintain image details. In addition, iterative impulse noise filters operate more efficiently.     

Distributed bias-compensated normalized least-mean squares algorithms with noisy input

In this paper, we study the problem of distributed normalized least-mean squares (NLMS) estimation over multi-agent networks, where all nodes collaborate to estimate a common parameter of interest. We consider the situations that all nodes in the network are corrupted by both input and output noise. This yields into biased estimates by the distributed NLMS algorithms. In our analysis, we take all the noise into consideration and prove that the bias is dependent on the input noise variance. Therefore, we propose a bias compensation method to remove the noise-induced bias from the estimated results. In our development, we first assume that the variances of the input noise are known a priori and develop a series of distributed-based bias-compensated NLMS (BCNLMS) methods. Under various practical scenarios, the input noise variance is usually unknown a priori, therefore it is necessary to first estimate for its value before bias removal. Thus, we develop a real-time estimation method for the input noise variance, which overcomes the unknown property of this noise. Moreover, we perform some main analysis results of the proposed distributed BCNLMS algorithms. Furthermore, we illustrate the performance of the proposed distributed bias compensation method via graphical simulation results.     

A Variational Approach to Remove Outliers and Impulse Noise

We consider signal and image restoration using convex cost-functions composed of a non-smooth data-fidelity term and a smooth regularization term. We provide a convergent method to minimize such cost-functions. In order to restore data corrupted with outliers and impulsive noise, we focus on cost-functions composed of an ?₁ data-fidelity term and an edge-preserving regularization term. The analysis of the minimizers of these cost-functions provides a natural justification of the method. It is shown that, because of the ?₁ data-fidelity, these minimizers involve an implicit detection of outliers. Uncorrupted (regular) data entries are fitted exactly while outliers are replaced by estimates determined by the regularization term, independently of the exact value of the outliers. The resultant method is accurate and stable, as demonstrated by the experiments. A crucial advantage over alternative filtering methods is the possibility to convey adequate priors about the restored signals and images, such as the presence of edges. Our variational method furnishes a new framework for the processing of data corrupted with outliers and different kinds of impulse noise.