Iterative composite filtering for image restoration

An algorithm solution to the noisy image restoration problem under assumptions that the image is nonstationary and that the noise process is a superposition of white and impulsive noises is proposed. A composite model is used for the image in order to consider its nonstationarities, in the mean and the autocorrelation. Separating the gross information about the image from its textural information, the authors exploit the advantages of median, range, and Levinson filters in restoring the image. Median statistics are used to estimate the image's gross information and to filter the impulsive noise. Range statistics are used to segment the textural image into approximately locally stationary images to be filtered by Levenson filters. The proposed restoration algorithm adapts to the nonstationarity of the image, and, thus, it performs well. The algorithm is compared with others based on either median or linear filtering alone     

SAR image processing using adaptive stack filter

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters.     

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。     

Nonlinear restoration of noisy images

The restoration of images degraded by an additive white noise is performed by nonlinearly filtering a noisy image. The standard Wiener approach to this problem is modified to take into account the edge information of the image. Various filters of increasing complexity are derived. Experimental results are shown and compared to the standard Wiener filter results and other earlier attempts involving nonstationary filters.     

基于自适应堆滤波的边界检测

从被噪声干扰的图象中提取边界是图象测试与分析的关键之一。通常需要先滤除图象中的噪声，再用边界检测算子求出边界。本文介绍了一种边界直接检测法，即将边界检测与噪声滤波相结合，它是基于自适应堆滤波的边界检测法。首先非线性堆滤波器用于求出图象某象素点邻域内的灰度最大值与最小值的最优估计，然后以此两估计值之差代替原象素点灰度值。最后对之二值化求出边界。本文根据最小平均绝对误差准则，采用自适应方法求解堆滤波器。这种方法类似于线性自适应滤波器的ＬＭＳ方法，先任设一初始堆滤波器，利用期望图象与合噪声图象对堆滤波器进行迭代训练，最后求出最优化的自适应堆滤波器。文章最后给出了采用自适应堆滤波法求取图象边界的试验结果，表明这种方法可以有效地抑制各种分布的噪声干扰。     

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.     

3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising

Block matching based 3D filtering methods have achieved great success in image denoising tasks. However, the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network (CNN) for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model (BMCNN) could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio (PSNR) when denoising images with high noise levels (σ > 40), and the best visual quality when denoising images with all the tested noise levels.     

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。     

利用几何结构检测去除图像中的随机值脉冲噪声

尽管中值滤波以及各种改进方法是去除图像中随机值脉冲噪声的有效方法,然而,大多数去噪方法存在门限值选取困难和对图像边缘纹理结构过平滑的缺点。针对这一问题,提出了一种基于几何结构的用于检测和去除随机值脉冲噪声的新方法。该方法首先利用图像的直方图分布来估计脉冲噪声的噪声率;然后进一步基于噪声率和细节图像的直方图分布,自适应地确定两个分类门限;最后利用两个门限,将细节图像中的像素分成‘未被污染点’、‘待定点’和‘噪声点’。其中‘待定点’主要由边缘和纹理区像素和噪声像素构成,为区分其属性,还引入了几何结构检测方法。基于各像素点的类型,细节图像被用于修正中值滤波的结果。实验结果表明,该新方法在去除脉冲噪声的同时,还很好地保留了图像的边缘结构。与已有的方法相比,具有明显的优势。     

A new adaptive center weighted median filter for suppressing impulsive noise in images

In this work, a new adaptive center weighted median (ACWM) filter is proposed for improving the performance of median-based filters, preserving image details while effectively suppressing impulsive noise. The proposed filter is an adaptive CWM filter with an adjustable central weight obtained by partitioning the observation vector space. To obtain the optimal weight for each block, the efficient scalar quantization (SQ) method is used to partition the observation vector space. The center weight within each block is obtained by using a learning approach based on the least mean square (LMS) algorithm. The noise filtering procedure is progressively applied through several iterations so that the mean square error of the output can be minimized. Experimental results have demonstrated that the proposed filter outperforms many well-accepted median-based filters in terms of both noise suppression and detail preservation. The proposed new filter also provides excellent robustness at various percentages of impulsive noise.     

A Filter To Remove Gaussian Noise by Clustering the Gray Scale

An algorithm to suppress Gaussian noise is presented, based on clustering (grouping) gray levels. The histogram of a window sliding across the image is divided into clusters, and the algorithm outputs the mean level of the group containing the central pixel of the window. This filter restores well the majority of noisy pixels, leaving only few of them very deviated, that can be finally restored with a common filter for impulsive noise, such as a median filter. In this paper the clustering filter CF is described, analysed and compared with other similar filters.     

基于HVS特性的图像自适应中值滤波算法

通过考虑宿主图像亮度、纹理、边缘等特征,提出一种图像自适应中值滤波算法。该算法利用基于人眼视觉特性的临界噪声阈值确定噪声点,根据噪声密度自适应调整滤波窗口大小,采用改进的中值滤波对检测出的噪声点进行处理,从而在去除噪声的同时较好地保护图像细节。实验结果表明,该算法比传统中值滤波及其改进算法有更好的滤波性能,对于噪声污染严重的图像,滤波效果更好。     

A new fuzzy logic filter for image enhancement

This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.     

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.     

Nonlinear filtering structure for image smoothing in mixed-noise environments

This paper introduces a new nonlinear filtering structure for filtering image data that have been corrupted by both impulsive and nonimpulsive additive noise. Like other nonlinear filters, the proposed filtering structure uses order-statistic operations to remove the effects of the impulsive noise. Unlike other filters, however, nonimpulsive noise is smoothed by using a maximum a posteriori estimation criterion. The prior model for the image is a novel Markov random-field model that models image edges so that they are accurately estimated while additive Gaussian noise is smoothed. The Markov random-field-based prior is chosen such that the filter has desirable analytical and computational properties. The estimate of the signal value is obtained at the unique minimum of the a posteriori log likelihood function. This function is convex so that the output of the filter can be easily computed by using either digital or analog computational methods. The effects of the various parameters of the model will be discussed, and the choice of the predetection order statistic filter will also be examined. Example outputs under various noise conditions will be given.     

Self-adaptive algorithm of impulsive noise reduction in color images

In this paper a new approach to the problem of impulsive noise reduction in color images is presented. The basic idea behind the new image filtering technique is the maximization of the similarities between pixels in a predefined filtering window. The improvement introduced to this technique lies in the adaptive establishing of parameters of the similarity function and causes that the new filter adapts itself to the fraction of corrupted image pixels. The new method preserves edges, corners and fine image details, is relatively fast and easy to implement. The results show that the proposed method outperforms most of the basic algorithms for the reduction of impulsive noise in color images.     

中子集型自适应中值滤波器

消除脉冲噪声通常采用中值滤波算法。尽管有许多中值滤波方法做了很大改进,但是,在噪声密度较高的情况下,图像滤波的结果仍然不能令人满意,因此,提出了一种新型中值算法。首先对滤波窗口中的像素进行分类,然后确定其中多元素子集的中子集,并且根据多元素子集的个数来决定是进行滤波还是扩大滤波窗口。最后,在仿真中,将该算法分别和几种中值滤波算法在数值和视觉上进行比较,实验结果显示,该算法能够有效地降低脉冲噪声并且保留了原始图像的更多细节。     

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.     

A hybrid image restoration approach: fuzzy logic and directional weighted median based uniform impulse noise removal

In this paper, a hybrid image restoration technique based on fuzzy logic and directional weighted median is presented. The proposed technique consists of noise detection and fuzzy filtering processes to detect and remove uniform (random-valued) impulse noise while preserving the image details efficiently. In order to preserve image details such as edges and texture information, a two-stage robust noise detection is presented in this paper. Pixels detected as noisy by both the noise detection stages are considered for noise removal by the fuzzy filtering process, which utilizes the direction based weighted median to construct fuzzy membership function, which is the main contributing factor in noise removal and detail preservation. Extensive experimentation shows that the proposed technique performs significantly better than state-of-the-art filters based on peak signal-to-noise ratio, structural similarity index measure and subjective evaluation criteria.