No-reference image quality assessment based on noise,blurring and blocking effect

Since existing no-reference image quality assessment (IQA) algorithms are not consistent with subjective assessment, a novel no-reference image quality assessment method is proposed by introducing three types of image distortion, including noise, blur degree and blocking effects. Firstly, the standard deviation of image noise is estimated by modified wavelet medium estimation. Secondly, the blur degree of image is obtained by counting edge pixel points. Thirdly, blocking effect is represented by characteristics of image pixel blocks. Finally, the assessment model is established by combining these three distortion types. Combining the differential mean opinion scores (DMOS) provided in the LIVE IQA database, the weighting coefficients are obtained. The experimental results indicate that these evaluation values of this algorithm not only agree with PSNR in objective assessment, but also are consistent with the DMOS in subjective assessment.     

点光源哈特曼最优阈值估计方法研究

针对夏克-哈特曼波前传感器探测系统中噪声随时间及空间变化频率较快的特点,为了准确估计系统的最优阈值,根据高斯光斑与噪声的分布特性,提出一种以滑动窗口内像素均值及图像信号的局部梯度作为参数,构造关于噪声权重函数的方法,由此获得子孔径阈值的最优估计值,并详细分析了算法的基本原理和实现过程.以典型处理方法获取的阈值与理论最优阈值的误差作为评价标准,仿真和实验结果表明本文提出的阈值估计方法在不同信噪比、不同光斑大小的条件下,均能取得优于典型阈值处理方法获得的结果,且与理论最优阈值的误差小于10%.     

基于改进开关中值滤波的多孔网栅图像脉冲噪声消除

针对多孔网栅闪光照相图像含有随机脉冲噪声的问题,提出了一种改进的开关中值滤波噪声消除算法。该算法利用像素与邻域窗口统计中值的灰度信息,建立噪声点探测器。通过设置噪声点探测阈值来识别噪声,并用邻域窗口内统计中值代替噪声点取值。经过多次滤波,含随机脉冲噪声的计算机合成网栅图像及实验网栅图像可获得良好的恢复效果。     

Nonlinear averaging reconstruction method for phase-cycle SSFP

The ability to obtain high-quality images of small structures, such as the nerves of the inner ear, is important for the early diagnosis of numerous conditions. Balanced steady-state free precession (SSFP; e.g., true fast imaging with steady-state precession) is a fast acquisition method, but its use has been limited by the presence of off-resonance banding artifacts. To reduce these artifacts multiacquisition balanced SSFP with phase cycling is used, yielding multiple data sets in which the banding artifacts are spatially shifted with respect to each other (e.g., as in CISS). We present a new method, called nonlinear averaging (NLA), for combining these data sets to reduce banding artifacts. The NLA method arithmetically averages the three highest magnitude signals from four-phase-cycle SSFP data on a pixel-by-pixel basis. Simulations indicate that NLA offers improved signal-to-noise ratio (SNR) over the more standard maximum intensity projection (MIP) reconstruction. NLA is compared to MIP in simulations and volunteer tests. Simulations suggest that NLA provides substantially improved SNR compared to MIP. In a randomized blinded comparison of 10 volunteer studies, two radiologists found that NLA, compared to MIP, gave improved results. NLA also provided superior noise reduction and enhanced edge sharpness compared to MIP. We demonstrate that NLA, similar to MIP, improves SNR and image quality. It does so consistently in all situations to which it is applied.     

Estimation and application of spatially variable noise fields in diffusion tensor imaging

Optimal interpretation of magnetic resonance image content often requires an estimate of the underlying image noise, which is typically realized as a spatially invariant estimate of the noise distribution. This is not an ideal practice in diffusion tensor imaging because the noise distribution is usually spatially varying due to the use of fast imaging and noise suppression techniques. A new estimation approach for spatially varying noise fields (NFs) is proposed in this article. The approach is based on a noise invariance property in scenarios in which more than one image, each with potentially different signal levels, is acquired on each slice, as in diffusion-weighted MRI. This technique leads to improved NF estimates in simulations, phantom experiments and in vivo studies when compared to traditional NF estimators that use regional variability or background intensity histograms. The proposed method reduces the NF estimation error by a factor of 100 in simulations, shows a strong linear correlation (R²=0.99) between theoretical and estimated noise changes in phantoms and demonstrates consistent (<5% variability) NF estimates in vivo. The advantages of spatially varying NF estimation are demonstrated for power analysis, outlier detection and tensor estimation.     

高噪声背景下激光条纹亚像素中心的提取

针对线结构光三维形貌测量中大量噪声易干扰激光条纹中心提取准确度的问题,提出了一种条纹亚像素中心提取方法。分析条纹图像中的噪声,采用平均法和中值滤波预处理图像;利用迭代阈值分割及形态学方法,获取条纹目标,引入距离变换提取条纹的像素级中心;根据像素级中心、二值信息及光强灰度,结合曲线拟合及重心法精确提取条纹的亚像素中心。仿真分析和实验验证下,相邻行条纹中心列坐标最大偏差值像素小于2 ,平均偏差像素值约为0.3,与传统方法相比,2项指标值更小。实验结果表明,算法有效利用条纹灰度分布规律,可降低噪声对中心定位精度的影响,更逼近条纹真实中心位置,抗噪能力极强。     

非局部变分修复法去除高密度椒盐噪声

分析了中值滤波及其改进型算法在处理高密度椒盐噪声时效果不理想的原因,采用变分修复方法来去除高密度椒盐噪声,基于现有的全变差修复模型提出了非局部全变差修复模型。该模型利用椒盐噪声特点(均匀分布、灰度值为0或255),将噪声点看成是图像中遗失或是破损的点,首先在图像中寻找与噪声点邻域相似的区域,将相似区域的中心像素作为噪声点新的邻域然后对其插值,把图像降噪问题转化为图像修复问题,从而达到去除高密度噪声的目的。实验结果表明:该模型对噪声密度为90%的彩色和灰度图像去噪后,其峰值信噪比为22.85和28.77,在客观评价标准方面优于中值滤波及其改进型算法。该模型能有效去除高密度下的椒盐噪声并较好地恢复图像细节,为图像去除高密度噪声提供了一种新的途径。     

自适应非凸稀疏正则化下自适应光学系统加性噪声的去除

自适应光学系统可以实时测量并校正波前信息,但是系统中大量的噪声严重影响了系统的探测精度.自适应光学系统中一般为加性噪声,本文提出一种全新的变分处理模型去除加性噪声,该模型采用自适应非凸正则项.非凸正则项在保持图像细节上较凸正则项具有更好的效果,能更好地保持点源目标的完整性.另外,根据不同区域的噪声水平自适应地构建正则化参数,使不同区域的像素点受到不同程度的噪声抑制,可以更好地保持目标的边缘细节.在算法实现上,为了解决非凸正则项收敛性较差的缺陷,采用分裂Bregman算法及增广拉格朗日对偶算法进行计算.实验及数值仿真结果都表明,该方法能够较好地去除系统中的加性噪声,且光斑信号保存得较为完整,处理后的质心探测精度及信噪比较高.     

Time varying forgetting factor for the noise estimation in multi-channel noise reduction

We introduce a time varying forgetting factor for the noise estimation in multi-channel noise reduction. In conventional multi-channel noise reduction system, the noise statistics are estimated during noise-only periods and kept fixed during speech-present periods. For deciding whether the current frame is speech-present period or not, a voice activity detector (VAD) is usually used. Instead of this conventional scheme that needs an explicit VAD, we adopt a time varying forgetting factor which is parameterized from the normalized cross correlation (NCC). The simulation results show that multi-channel noise reduction using the proposed method yields better performance in several typical objective measures.     

MIRE-IL methodology applied to measuring the noise attenuation of earmuff hearing protectors

This article describes an objective methodology for measuring the noise attenuation of earmuff hearing protectors using as a reference the method known as microphone-in-real-ear (MIRE). The methodology implements the insertion loss (IL) paradigm, in which IL is measured using miniature microphones, specially designed to comply with ANSI and ISO standards for the MIRE technique. The results for four different hearing protectors are compared with the subjective method known as real-ear-attenuation-at-threshold (REAT). Correction factors are included in the methodology to account for external effects such as physiological noise and bone conduction. The objective method predicted well the real protection of the earmuffs and the proposed methodology showed lower standard deviation values than the REAT method.     

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.     

基于多途信道的舰船辐射噪声功率谱密度估计*

针对低信噪比下利用单水听器估计辐射噪声功率谱密度精度较差的问题,提出一种基于多途信道传输函数估计的垂直阵测量估计1 m处舰船辐射噪声功率谱密度的方法。该方法将信道传输函数表示为多途路径近场阵列流形向量的叠加,较快地估计了信道传输函数,将其用于舰船辐射噪声功率谱密度估计,可较简便地估计距声中心1 m处辐射噪声的功率谱密度,即谱源级。分析了产生功率谱密度估计误差的原因,包括信道估计误差和环境噪声引起的误差,为降低估计误差提供了理论依据。仿真结果表明,该方法估计1 m处辐射噪声功率谱密度的性能良好。     

Complex noise suppression using a sparse representation and 3D filtering of images

A novel method for the filtering of images corrupted by complex noise composed of randomly distributed impulses and additive Gaussian noise has been substantiated for the first time. The method consists of three main stages: the detection and filtering of pixels corrupted by impulsive noise, the subsequent image processing to suppress the additive noise based on 3D filtering and a sparse representation of signals in a basis of wavelets, and the concluding image processing procedure to clean the final image of the errors emerged at the previous stages. A physical interpretation of the filtering method under complex noise conditions is given. A filtering block diagram has been developed in accordance with the novel approach. Simulations of the novel image filtering method have shown an advantage of the proposed filtering scheme in terms of generally recognized criteria, such as the structural similarity index measure and the peak signal-to-noise ratio, and when visually comparing the filtered images.     

A subpixel motion estimation algorithm based on digital correlation for illumination variant and noise image sequences

One of the challenges in practical subpixel motion estimation is how to obtain high accuracy with sufficient robustness to both illumination variations and additive noise. Motivated by the fact that the normalized spatial cross-correlation is invariant to illumination, we introduce a gradient-based subpixel registration method by maximizing the digital correlation (DC) function between the reference and target frames. Such DC function is remodeled with the presence of image noise, yielding that the correlation coefficient is only sensitive to noise standard variance. To fairly suppress the noise corruption, not only the target frame but also the reference one is reformulated into Taylor gradient expression with half but opposite motion vector. The final solution to motion estimates can be approximated into a closed form by reserving first-order coefficient terms of unregistered motion variables. The error trend of approximated solution is discussed. Computer simulations and actual experiments’ results demonstrate the superiority of the proposed method to the LMSE-based method and ordinary DC method when illumination variations and noise exist. Among the experiments, the influences of real subpixel translation value and noise variance degree on accuracy are studied; correspondingly, an optimized iterative idea for big translations and the recommended noise level adaptive to our method are introduced.     

Estimation of random errors for lidar based on noise scale factor

Estimation of random errors, which are due to shot noise of photomultiplier tube(PMT) or avalanche photodiode(APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electrons, there still exists a proportional relationship between standard deviation and square root of its mean value. Based on this relationship,noise scale factor(NSF) is introduced into the estimation, which only needs a single data sample. This method overcomes the distractions of atmospheric fluctuations during calculation of random errors. The results show that this method is feasible and reliable.     

光纤和半导体光放大器的非线性过程噪声特性的一致性研究

提出一种在光信号上叠加正态分布研究随机扰动的方法,分别研究了光纤中的拍信号演化和SOA中的光脉冲演化。光纤与SOA分别是无源器件和有源器件,在这两种不同元件中,光信号的传输是两种完全不同的过程。得到了随机扰动对这两种不同过程中光信号传输的影响,计算了输出相对标准差。结果表明,该方法在研究非线性过程及其相关的全光信号处理中,可用于讨论器件或系统的噪声特性,从而研究输出光脉冲的噪声特性。     

A novel feedback active noise control for broadband chaotic noise and random noise

The feedback active noise control (ANC) can be seen as a predictor, the conventional method based on filtered-x least mean square (FXLMS) algorithm can only be useful for linear and tonal noise, but for nonlinear and broadband noise, it is useless. The feedback ANC using functional link artificial neural networks (FLANN) based on filtered-s least mean square (FSLMS) algorithm can reduce some nonlinear noise such as chaotic noise, but the noise cancellation performance is not very well, at the same time, it is not useful to random noise. To solve the problem above, a new feedback ANC using wavelet packet FXLMS (WPFXLMS) algorithm is proposed in this paper. By decomposing the broadband noise into several band-limited parts which are predictable and each part is controlled independently, the proposed algorithm can not only suppress the chaotic noise, but also mitigate the random noise. Compared with FXLMS and FSLMS algorithms, proposed WPFXLMS algorithm also holds the best performance on noise cancellation. Numerous simulations are conducted to demonstrate the effectiveness of the proposed WPFXLMS algorithm.     

Application of an acoustic noise removal method to aircraft-based atmospheric temperature measurements

An acoustic noise removal method is used to reject engine acoustical disturbances from aircraft-based atmospheric temperature measurements. Removal of engine noise from atmospheric temperature measurements allows a larger wave number range to be fit while quantifying the magnitude of atmospheric temperature turbulence. The larger wave number range was found to result in a more statistically certain spectral slope estimate, with up to a 50% reduction in the standard deviation of measured spectral slopes. The noise removal technique was found to break down under conditions of weak atmospheric temperature turbulence where the engine acoustical disturbance can be several orders of magnitude larger than atmospheric temperature turbulence.