彩色图像脉冲噪声的模糊检测和滤波

提出了一种新的彩色图像脉冲噪声滤除的方法。该方法充分考虑彩色图像的颜色信息,结合模糊规则进行检测。实验结果显示,此方法不论对椒盐脉冲噪声还是均匀分布的随机值脉冲噪声都有较好的滤波效果,比VMF更好地保护了图像边缘细节。     

A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images

A new impulse noise detector based on neuro-fuzzy methods is presented. The proposed detector comprises two identical neuro-fuzzy subdetectors combined with a decision maker. The internal parameters of the subdetectors are adaptively adjusted by training. Training of the subdetectors is accomplished by using a simple computer generated artificial image. The detector can be combined with any impulse noise removal operator. The operation of the detector is completely independent of the noise removal operator and it has no influence on the filtering behavior of the operator. Experimental results show that the proposed detector significantly reduces the distortion effects of any impulse noise removal operator even if the operator already has its own noise detector.     

Removal of impulse noise in color images based on convolutional neural network

A new denoising framework based on deep convolutional neural network for suppressing impulse noise in color images is proposed in this paper. The proposed framework consists of two modules: noise detection and image reconstruction, both of which are implemented by a deep convolutional neural network. First, a noise classifier network is trained to detect random-valued impulse noise in a color image, which not only can detect the noisy color vector pixels but also can further identify the corrupted channels of each noisy color pixel. Then, a sparse clean color image is computed by replacing the values of noisy channels with 0 and keeping other noise-free channels unchanged. Finally, the sparse clean color image is fed to another denoiser network to reconstruct the denoised image. Experimental results show that the proposed denoiser outperforms other state-of-the-art methods clearly in both performance measure and visual evaluation.     

Algorithms for impulse noise removal from corrupted color images

Two effective algorithms for the removal of impulse noise from color images are proposed. The algorithms consist of two steps. The first algorithm detects outliers with the help of spatial relations between the components of a color image. Next, the detected noise pixels are replaced with the output of a vector median filter over a local spatially connected area excluding the outliers, while noise-free pixels are left unaltered. The second algorithm transforms a color image to the YCbCr color space that perfectly separates the intensity and color information. Then outliers are detected using spatial relations between transformed image components. The detected noise pixels are replaced with the output of a modified vector median filter over a spatially connected area. Simulation results in test color images show a superior performance of the proposed algorithms compared with the conventional vector median filter. The comparisons are made using the mean square error, the mean absolute error, and a subjective human visual error criterion. This article was translated by the authors. Vitaly Kober obtained his MS degree in applied mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in image processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at the Centre de Investigación Cientifica y de Educacion Superior de Ensenada (Cicese), México. His research interests include signal and image processing, pattern recognition. Mikhail Mozerov received his MS degree in physics from Moscow State University in 1982 and his PhD degree in image processing from the Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He works at the Laboratory of Digital Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include signal and image processing, pattern recognition, digital holography. Alvarez-Borrego Josué obtained his MS degree in optics from the Centro de Investigatión Científica y de Educatión Superior de Ensenada (Cicese), México, in 1983, and his PhD degree in optics from the Cicese in 1993. He is a titular researcher at the Cicese. His research interests include image processing and pattern recognition applied to study marine surfaces, statistical and biogenic particles. He has more than 25 scientific papers. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral degree in information theory in 1972. At present, he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

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.     

Quantum and impulse noise filtering from breast mammogram images

Recent advances in the field of image processing have shown that level of noise highly affect the quality and accuracy of classification when working with mammographic images. In this paper, we have proposed a method that consists of two major modules: noise detection and noise filtering. For detection purpose, neural network is used which effectively detect the noise from highly corrupted images. Pixel values of the window and some other features are used as feature for the training of neural network. For noise removal, three filters are used. The weighted average value of these three filters is filled on noisy pixels. The proposed technique has been tested on salt & pepper and quantum noise present in mammogram images. Peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) are used for comparison of proposed technique with different existing techniques. Experiments shows that proposed technique produce better results as compare to existing methods.     

自适应中值滤波算法滤除医学图像脉冲噪声

实现了一种滤除医学图像脉冲噪声的自适应中值滤波算法,用均方根误差和噪声对原图像的毁坏程度两个客观评价指标对该算法及传统均值、中值滤波方法进行了比较与评价。根据设定条件检测滤波窗口中心像素是否为脉冲噪声,采取滤波窗口自适应的算法来滤除脉冲噪声,去除了传统中值滤波对所有像素均用中值代替的弊端,减少了不必要的图像细节损失。基于MATLAB的仿真实验表明,对于较大密度的脉冲噪声,该算法在有效抑制噪声的同时,能较好地保护边缘和细节信息。该算法已应用于虚拟内窥镜系统中,取得了令人满意的效果。     

图像脉冲噪声滤波算法

针对低噪声污染图像提出了一种改进的中值滤波算法.该算法通过计算滑动窗口内的像素均值和方差,根据数理统计特性,自适应选定阈值,对符合噪声条件的像素进行初次滤除,然后采用开关中值滤波算法对不符合条件的像素再次滤波.实验结果表明,该算法既能有效地去除噪声,又能清晰地保持图像边缘,降低了传统改进型中值滤波算法对阚值的依赖性和对图像边缘细节的损害程度,且滤波性能优于一些典型改进型中值滤波算法.     

图像脉冲噪声的自适应长距离相关迭代滤波法

提出一种基于局部极值噪声检测的自适应长距离相关迭代滤波算法.该算法首先采用局部极值法进行噪声检测,然后在一定的搜寻范围内计算信号点与噪声点的背景均方误差值,并以该背景均方误差值为基础采用自适应加权法进行滤波,最后将这一滤波过程进行迭代计算.实验结果表明,该算法滤波效果优于传统的滤波算法,它可以有效地去除图像中的脉冲噪声,并较好地保持图像细节信息,在噪声密度很大的情况下也表现出很好的滤波性能.     

An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images

The latest-generation earth observation instruments on airborne and satellite platforms are currently producing an almost continuous high-dimensional data stream. This exponentially growing data poses a new challenge for real-time image processing and recognition. Making full and effective use of the spectral information and spatial structure information of high-resolution remote sensing image is the key to the processing and recognition of high-resolution remote sensing data. In this paper, the adaptive multipoint moment estimation (AMME) stochastic optimization algorithm is proposed for the first time by using the finite lower-order moments and adding the estimating points. This algorithm not only reduces the probability of local optimum in the learning process, but also improves the convergence rate of the convolutional neural network (Lee Cun et al. in Advances in neural information processing systems, 1990). Second, according to the remote sensing image with characteristics of complex background and small sensitive targets, and by automatic discovery, locating small targets, and giving high weights, we proposed a feature extraction method named weighted pooling to further improve the performance of real-time image recognition. We combine the AMME and weighted pooling with the spatial pyramid representation (Harada et al. in Comput Vis Pattern Recognit 1617–1624, 2011) algorithm to form a new, multiscale, and multilevel real-time image recognition model and name it weighted spatial pyramid networks (WspNet). At the end, we use the MNIST, ImageNet, and natural disasters under remote sensing data sets to test WspNet. Compared with other real-time image recognition models, WspNet achieve a new state of the art in terms of convergence rate and image feature extraction compared with conventional stochastic gradient descent method [like AdaGrad, AdaDelta and Adam (Zeiler in Comput Sci, 2012; Kingma and Ba in Comput Sci, 2014; Duchi et al. in J Mach Learn Res 12(7):2121–2159, 2011] and pooling method [like max-pooling, avg-pooling and stochastic-pooling (Zeiler and Fergus in stochastic-pooling for regularization of deep convolutional neural networks, 2013)].     

Intelligent noise detection and filtering using neuro-fuzzy system

In this paper, we propose a neuro-fuzzy based blind image restoration to remove impulse noise from low as well as highly corrupted images. Main components of the proposed technique include noise detection, histogram estimation and noise filtering process. Proposed technique constructs the fuzzy sets using fuzzy number construction algorithm. These fuzzy sets are used in noise filtering process to remove impulse noise from the noisy pixels using neuro-fuzzy inference system and fuzzy decider. Experimental results are based on global and local error measures, which prove that the proposed technique gives superior results than the present well known impulse noise filtering methods.     

Hybrid robust watermarking for color images

In this paper, we present a blind and highly robust watermarking scheme method for color images by combining the advantages of both spatial and frequency domain. Watermark is generated for each channel (RGB) of the color image by extracting spatial domain features using Gray Level Co-occurence Matrix as well as a unique identification number. The watermark is embedded in Principal Component Analysis (PCA)-based less correlated low and high frequency sub bands in such a way that the perceptual quality of the image is preserved. Imperceptibility is achieved by embedding the watermark in less correlated sub bands and robustness is achieved by spreading the watermark using Laplacian Pyramid in contourlet transform. Simulation results show that the proposed scheme can survive various image processing and signal processing attacks. The proposed method achieves high transparency, imperceptibility and robustness compared to some of the existing schemes.     

Multiclass SVM based adaptive filter for removal of high density impulse noise from color images

This paper proposes a multiclass support vector machine (SVM) based adaptive filter for removal of impulse noise from color images. The quality of the image gets degraded due to the presence of impulse noise. As a result, the homogeneity amongst the pixels gets distorted that needs to be restored. The feature set comprising of prediction error, difference between the median value and the center pixel; the median value in the kernel under operation has been used during this study. The pixel of test image is processed using adaptive window based filter that depends on the associated class assigned at the testing phase. The baseline system has been designed using modified histogram based fuzzy color filter (MHFC) technique. Four set of experiments have been carried out on a large database to validate the proposed method. The performance of the technique have been evaluated using peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). The results suggest that for fixed valued impulse noise, the proposed filter performs better than the MHFC in case of high density impulse noise (>45%). However, for random valued impulse noise the proposed filter outperforms the MHFC based method for both low and high density of noise. The objective analysis suggests that there is ∼3 dB improvement in PSNR as compared to the MHFC based method for high density of impulse noise. The results of SSIM along with visual observations indicate that the image details are maintained significantly in the proposed technique as compared to existing methods.     

A probabilistic approach to multivariable robust filtering andopen-loop control

A new approach to robust filtering, prediction, and smoothing of discrete-time signal vectors is presented. Linear time-invariant filters are designed to be insensitive to spectral uncertainty in signal models. The goal is to obtain a simple design method, leading to filters which are not overly conservative. Modeling errors are described by sets of models, parameterized by random variables with known covariances. These covariances could either be estimated from data or be used as robustness “tuning knobs.” A robust design is obtained by minimizing the ℋ₂-norm or, equivalently, the mean square estimation error, averaged with respect to the assumed model errors. A polynomial solution, based on an averaged spectral factorization and a unilateral Diophantine equation, is derived. The robust estimator is referred to as a cautious Wiener filter. It turns out to be only slightly more complicated to design than an ordinary Wiener filter. The methodology can be applied to any open-loop filtering or control problem. In particular, we illustrate this for the design of robust multivariable feedforward regulators, decoupling and model matching filters     

自适应控制迭代的随机值脉冲噪声滤波

定义了一种更接近实际噪声情况的随机值脉冲噪声模型,针对随机值脉冲噪声的滤除提出一种自适应控制迭代次数的滤波算法。算法包括噪声检测、噪声滤除、误检像素修正和迭代控制四个步骤,对固定值脉冲噪声和随机值脉冲噪声均能有效滤除。与中值滤波算法进行实验比较,在滤除随机值脉冲噪声时,该算法滤波后图像细节信息保护较好,且滤波过程无需设定迭代参数,自适应性强。     

A robust and lossless DNA encryption scheme for color images

In this paper, a new robust and lossless color image encryption algorithm is presented based on DNA sequence operation and one-way coupled-map lattices (OCML). The plain-image is firstly decomposed into three gray-level components and we randomly convert them into three DNA matrices by the DNA encoding rules. Then the XOR operation is performed on the DNA matrices for two times. Next, the shuffled DNA matrices are transformed into three gray images according to the DNA decoding rules. Finally, a diffusion process is further applied to change the image pixel’s values by a key stream, and the cipher-image is attained. The key stream generated by OCML is related to the plain-image. Experimental results and security analysis demonstrate that the proposed algorithm has a good encryption effect and can withstand various typical attacks. Furthermore, it is robust against some common image processing operations such as noise adding, cropping, JPEG compression etc.

     

空域彩色图像鲁棒零水印算法

针对传统变换域水印算法往往通过修改变换域系数来嵌入水印信号,影响图像不可感知性的问题,利用载体图像整体均值与分块均值之间大小关系的稳定性,提出一种新的空域彩色图像鲁棒零水印算法。算法直接在空域通过整体图像均值与分块均值之间的关系构造特征矩阵,之后将此特征矩阵与预处理后的水印信息进行异或运算构造零水印信息,预处理之后再注册到知识产权数据库里。提取水印信息时,通过投票策略得到最终提取的水印信息。大量的仿真实验结果表明,该算法对常规的信号处理攻击、行列平移、尺寸缩放和旋转等几何攻击具有较强的鲁棒性。与相似的鲁棒水印算法相比,对于大多数的攻击,该算法具有更优越的性能。     

Densely connected network for impulse noise removal

Pattern Analysis and Applications - Recently, a new convolutional neural network (CNN) architecture, dubbed as densely connected convolutional network (DenseNet), has shown excellent results on...     

Iterative scheme-inspired network for impulse noise removal

Pattern Analysis and Applications - This paper presents a supervised data-driven algorithm for impulse noise removal via iterative scheme-inspired network (IIN). IIN is defined over a data flow...