一种基于多尺度噪声检测的图像中值滤波器

介绍了标准中值滤波与有效中值滤波的概念,提出了一种基于自适应多尺度噪声检测的中值滤波器,可用于恢复被椒盐脉冲噪声污染了的图像。滤波器将输入图像像素分为有效信号类、脉冲噪声类和恒定区域类,对各类像素采用不同的方法进行滤波处理。实验结果证明,本文算法的性能比现存的其它许多算法有了显著的提高,而且便于实现。     

一种带脉冲噪声图像的图像分割方法*

提出一种利用区域面积调节脉冲耦合神经网络神经元的脉冲发放值的计算方法,对带脉冲噪声的图像进行图像分割,在消除噪声影响的同时,提高了图像分割的自适应性和准确性.实验表明,该算法效果明显.     

噪声控制的图像非线性锐化方法

图像锐化是一种补偿轮廓,突出边缘信息以使图像更为清晰的处理方法。常规的锐化算法对图像进行高频增强时,结果会呈现明显噪声,因此提出了一种基于噪声控制的非线性锐化增强方法。针对图像边缘细节和噪声难以区别对待的缺点,该算法通过控制非线性函数中的参数来有效地将噪声和边缘分开处理。实验证明,该方法不仅能够锐化图像边缘,而且改善了传统锐化算法对图像噪声放大的缺点,经算法处理后的图像细节丰富,峰值信噪比和DV/BV值较高,具有良好的视觉效果,很好地改善了图像质量。     

A new heuristic approach for non-convex optimization problems

In this work a new optimization method, called the heuristic Kalman algorithm (HKA), is presented. This new algorithm is proposed as an alternative approach for solving continuous, non-convex optimization problems. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other metaheuristics, lies in the small number of parameters that need to be set by the user. Further, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA was evaluated in detail using several non-convex test problems, both in the unconstrained and constrained cases. The results were then compared to those obtained via other metaheuristics. The numerical experiments show that HKA is a promising approach for solving non-convex optimization problems, particularly in terms of computation time and success ratio.     

一种新颖的虹膜图像噪声检测方法*

为提高虹膜识别的正确率,针对虹膜图像中存在着眼睫毛和眼睑这两类较难检测的遮挡噪声,在分析现有检测虹膜噪声算法的优缺点后,提出了一套新颖的虹膜图像噪声检测方法：基于Gabor滤波变换的灰度均值法检测睫毛和利用最小二乘法检测眼睑。实验表明,该算法能有效地检测两种遮挡噪声,准确率分别达到95.10%和96.51%,且等错率（EER）指标与已有算法相比最优,提高了虹膜识别系统的整体性能。     

Linear quadratic regulation problem for discrete-time systems with multi-channel multiplicative noise

This paper investigates the linear quadratic regulation (LQR) problem for discrete-time systems with multiplicative noise. Multiplicative noise is usually assumed to be a scalar in existing literature works. Motivated by recent applications of networked control systems and MIMO communication technology, we consider multi-channel multiplicative noise represented by a diagonal matrix. We first show that the finite horizon LQR problem can be solved using a generalized Riccati equation. We then prove the convergence of the generalized Riccati equation under the conditions of stabilization and exact observability, and obtain the solution to the infinite horizon LQR problem. Finally, we provide a numerical example to demonstrate the proposed approach.     

一种基于平面拟合的图像恢复方法

论述了在进行实际图像处理时所使用的一种对于不均匀光照图像进行恢复的方法。该方法基于平面拟合来估算出实际的光照强度分布,并根据图像各点的反射系数不变这一特性来对图像进行修正。实验结果表明,该方法有效地克服了不均匀光照所造成的对于图像处理的不利影响。     

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。     

A new approach for image databases design

This paper focuses on the methodologies to organize and structure image databases. Conventional relational database techniques are optimized to deal with textual and numeric data; however, they are not effective to handle image data. Some progresses have been made in developing new approaches to establish and use image databases, but the applications of these approaches are very labor-intensive, error-prone, and impractical to large-scale databases. In this paper, we propose a new approach to develop the structure of a large-scale image automatically. It is an integrated approach from existing technologies for the new application where the management of image data is focused. In addition, we present a solution to data indexing for the image database with different image types.     

A new restricted boltzmann machine training algorithm for image restoration

Multimedia Tools and Applications - A variety of approaches have been proposed for addressing different image restoration challenges. Recently, deep generative models were one of the mostly used...     

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.     

Kernel-based fourth-order diffusion for image noise removal

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourth-order diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.     

A maximum noise fraction transform with improved noise estimation for hyperspectral images

Feature extraction is often performed to reduce spectral dimension of hyperspectral images before image classification. The maximum noise fraction(MNF) transform is one of the most commonly used spectral feature extraction methods. The spectral features in several bands of hyperspectral images are submerged by the noise. The MNF transform is advantageous over the principle component(PC) transform because it takes the noise information in the spatial domain into consideration. However,the experiments describ...     

一种适用于远场噪声抑制的电台话音采集装置

为降低远场噪声对机载超短波电台话音采集的影响,基于麦克风阵列放大器LMV1090和单片机AT89C51重新设计了电台的话音采集装置,在其控制器内增加了模拟的话音增强电路。相比较数字语音增强技术,模拟技术省去了传统的信号放大和A/D、D/A转换电路,对电台硬件改动更少。软件采用单片机控制采集放大器的增益,具备较大的灵活性。试用结果表明,该话音采集装置对远场噪声有较好的抑制效果。     

A fuzzy filter for the removal of random impulse noise in image sequences

In this paper a new fuzzy filter for the removal of random impulse noise in digital grayscale image sequences is presented. The filter consists of different noise detection and filtering steps, in which the fuzzy set theory is used. This noise detection is based both on spatial and on temporal information and has the aim to prevent the filtering of noise free image pixels. The filtering of the detected noisy pixels is finally performed in a motion compensated way. Experimental results show that our method outperforms other state-of-the-art filters in terms of the peak-signal-to-noise ratio as well as visual quality.     

自回归滑动平均建模中观测噪声方差估计的新方法

目前的自回归滑动平均(ARMA)建模方法由于只利用了观测数据的高阶自协方差构建Yule-Walker方程,而没有利用观测数据的低阶自协方差信息,导致观测噪声方差的估计精度不高,并且在自回归(AR)阶次p小于或等于滑动平均(MA)阶次q时无法估计出观测噪声方差.为此,本文提出了一种单独估计观测噪声方差的新方法,即先将ARMA模型近似为一高阶AR模型,再构建从观测数据1阶自协方差开始的Yule-Walker方程.由于充分利用了观测数据的统计信息,有利于提高观测噪声方差的估计精度,为后续的AR和MA参数估计精度的提高奠定了基础,也解决了p小于或等于q时观测噪声方差无法估计的问题,仿真和实验结果验证了该方法的有效性.     

A noise robust gait representation: Motion energy image

Gait-based human identification aims to discriminate individuals by the way they walk. A unique advantage of gait as a biometric is that it requires no subject contact and is easily acquired at a distance, which stands in contrast to other biometric techniques involving face, fingerprints, iris, etc. This paper proposes a new gait representation called motion energy image (MEI). Compared with other gait features, MEI is more robust against noise that can be included in binary gait silhouette images due to various factors. The effectiveness of the proposed method for gait recognition is demonstrated using experiments performed on the NLPR database. Recommended by Editorial Board member Jang Myung Lee under the direction of Editor Jae-Bok Song. This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University. Grant Number: R11-2002-105-09002-0 (2009). Heesung Lee received the B.S. and M.S. degrees in Electrical and Electronic Engineering, from Yonsei University, Seoul, Korea, in 2003 and 2005, respectively. He is currently a Ph.D. candidate of Dept. of Electrical and Electronic Engineering at Yonsei University. His current research interests include computational intelligence, pattern recognition, biometrics, and neural network. Sungjun Hong received the B.S. degrees in Electrical and Electronic Engineering and Computer Science, from Yonsei University, Seoul, Korea, in 2005. He is a graduate student of the combined master’s and doctoral degree programs at Yonsei University. He has studied machine learning, biometrics and optimization Imran Fareed Nizami received the B.S. degree from University of Engg. & Tech. Taxila, Pakistan and the M.S. degree in the Electrical and Electronic Engineering from Yonsei University, Seoul, Korea. He is currently a senior lecturer in Bahria University, Islamabad, Pakistan. His research interests include biometrics, gait recognition, Bayesian and neural networks. Euntai Kim received the B.S. (with top honors), M.S. and Ph.D. degrees in Electronic Engineering from Yonsei University, Seoul, Korea, in 1992, 1994, and 1999, respectively. From 1999 to 2002, he was a Full-time Lecturer with the Department of Control and Instrumentation Engineering at Hankyong National University, Gyeonggi-do, Korea. Since 2002, he has been with the School of Electrical and Electronic Engineering at Yonsei University, where he is currently an associate professor. He was a Visiting Scholar with the University of Alberta, Edmonton, Canada, and the Berkeley Initiative in Soft Computing (BISC), UC Berkeley, USA, in 2003 and 2008, respectively. His current research interests include computational intelligence and machine learning and their application to intelligent service robots, unmanned vehicles, home networks, biometrics, and evolvable hardware.     

空间约束层次加权Gamma混合模型的SAR图像分割

目的 合成孔径雷达（SAR）图像中像素强度统计分布呈现出复杂的特性,而传统混合模型难以建模非对称、重尾或多峰等特性的分布。为了准确建模SAR图像统计分布并得到高精度分割结果,本文提出一种利用空间约束层次加权Gamma混合模型（HWGaMM）的SAR图像分割算法。方法 采用Gamma分布的加权和定义混合组份;考虑到同质区域内像素强度的差异性和异质区域间像素强度的相似性,采用混合组份加权和定义HWGaMM结构。采用马尔可夫随机场（MRF）建模像素空间位置关系,利用中心像素及其邻域像素的后验概率定义混合权重以将像素邻域关系引入HWGaMM,构建空间约束HWGaMM,以降低SAR图像内固有斑点噪声的影响。提出算法结合M-H（Metropolis-Hastings）和期望最大化算法（EM）求解模型参数,以实现快速SAR图像分割。该求解方法避免了M-H算法效率低的缺陷,同时克服了EM算法难以求解Gamma分布中形状参数的问题。结果 采用3种传统混合模型分割算法作为对比算法进行分割实验。拟合直方图结果表明本文算法具有准确建模复杂统计分布的能力。在分割精度上,本文算法比基于高斯混合模型（GMM）、Gamma分布和Gamma混合模型（GaMM）分割算法分别提高33%,29%和9%。在分割时间上,本文算法虽然比GMM算法多64 s,但与基于Gamma分布和GaMM算法相比较分别快600 s和420 s。因此,本文算法比传统M-H算法的分割效率有很大的提高。结论 提出一种空间约束HWGaMM的SAR图像分割算法,实验结果表明提出的HWGaMM算法具有准确建模复杂统计分布的能力,且具有较高的精度和效率。