Objective no-reference image blur metric based on local phase coherence

A novel algorithm for no-reference blur assessment in digital pictures is proposed. This metric is based on the evaluation of local phase coherence across the scales of an overcomplete wavelet transform. We compare the performance of the proposed method with four state-ofthe art metrics using a large database containing both simulated and real blur.     

去隔行的FPGA实现

提出了一种基于运动检测的去隔行算法。其原理是通过4场水平运动检测和场内插检测,将图像分为静止和运动两部分,并采用前场值和场内插值进行去隔行。文中的算法在FPGA上得以实现,并将结果在VGA上进行显示。通过观察去隔行图像,得知该方法能较好地消除模糊、锯齿等不良现象,获得了较为理想的效果     

旋转运动空间可变模糊图像的代数复原

提出了一种基于最小二乘和空间相关约束沿模糊路径卷积的代数复原方法。为了有效和快速地恢复角点处的图像信息,采用了最优估值理论。在微机上对算法进行了验证,实验结果表明,算法能有效地去除旋转运动的空间可变模糊。     

Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony

There is a growing interest in elucidating the role of specific patterns of neural dynamics--such as transient synchronization between distant cell assemblies--in brain functions. Magnetoencephalography (MEG)/electroencephalography (EEG) recordings consist in the spatial integration of the activity from large and multiple remotely located populations of neurons. Massive diffusive effects and poor signal-to-noise ratio (SNR) preclude the proper estimation of indices related to cortical dynamics from nonaveraged MEG/EEG surface recordings. Source localization from MEG/EEG surface recordings with its excellent time resolution could contribute to a better understanding of the working brain. We propose a robust and original approach to the MEG/EEG distributed inverse problem to better estimate neural dynamics of cortical sources. For this, the surrogate data method is introduced in the MEG/EEG inverse problem framework. We apply this approach on nonaveraged data with poor SNR using the minimum norm estimator and find source localization results weakly sensitive to noise. Surrogates allow the reduction of the source space in order to reconstruct MEG/EEG data with reduced biases in both source localization and time-series dynamics. Monte Carlo simulations and results obtained from real MEG data indicate it is possible to estimate non invasively an important part of cortical source locations and dynamic and, therefore, to reveal brain functional networks.     

Spatio-temporal cortical source imaging of brain electrical activity by means of time-varying parametric projection filter

In the present study, we explore suitable spatio-temporal filters for inverse estimation of an equivalent dipole-layer distribution from the scalp electroencephalogram (EEG) for imaging of brain electric sources. We propose a time-varying parametric projection filter (tPPF) for the spatio-temporal EEG analysis. The performance of this tPPF algorithm was evaluated by computer simulation studies. An inhomogeneous three-concentric-spheres model was used in the present simulation study to represent the head volume conductor. An equivalent dipole layer was used to represent equivalently brain electric sources and estimated from the scalp potentials. The tPPF filter was tested to remove time-varying noise such as instantaneous artifacts caused by eyes-blink. The present simulation results indicate that the proposed time-variant tPPF method provides enhanced performance in rejecting time-varying noise, as compared with the time-invariant parametric projection filter.     

Decomposition of magnetoencephalographic data into components corresponding to deep and superficial sources

We extend the signal space separation (SSS) method to decompose multichannel magnetoencephalographic (MEG) data into regions of interest inside the head. It has been shown that the SSS method can transform MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. In this paper, we show that the signal component obtained by the SSS method can be further decomposed by a simple operation into signals originating from deep and superficial sources within the brain. This is achieved by using a scheme that exploits the beamspace methodology that relies on a linear transformation that maximizes the power of the source space of interest. The efficiency and accuracy of the algorithm are demonstrated by experiments utilizing both simulated and real MEG data.     

精确提取InSAR时间去相关分量的方法

提出了一种干涉合成孔径雷达(InSAR,Synthetic Aperture Radar Interferometry)回波信号时间去相关分析的新方法,该方法主要包括三个步骤:采用自适应区域增长算法(IDAN,Intensity-Driven Adaptive Neighborhood)估计所有干涉子集的相干性;利用迭代最小二乘去除估计量偏差;采用相干性分解技术对无偏样本相干性进行分离,获得精确的时间去相关分量.以美国南加州洛杉矶地区的ENVISAT ASAR数据集为例,对新方法和现有方法进行了比较研究.结果表明,新的融合算法能够获得更加可靠、精度更高的时间去相关分量,并具有非阈值和近乎完全的自适应性.本文的研究将有利于改善与时间相干性有关的地球物理参数反演,也有利于地表变化周期性和随时间变化的气候环境实时监测等.     

Spatially variant defocus blur map estimation and deblurring from a single image

In this paper, we propose a single image deblurring algorithm to remove spatially variant defocus blur based on the estimated blur map. Firstly, we estimate the blur map from a single image by utilizing the edge information and K nearest neighbors (KNN) matting interpolation. Secondly, the local kernels are derived by segmenting the blur map according to the blur amount of local regions and image contours. Thirdly, we adopt a BM3D-based non-blind deconvolution algorithm to restore the latent image. Finally, ringing artifacts and noise are detected and removed, to obtain a high quality in-focus image. Experimental results on real defocus blurred images demonstrate that our proposed algorithm outperforms some state-of-the-art approaches.     

Effect of Head Shape Variations Among Individuals on the EEG/MEG Forward and Inverse Problems

We study the effect of the head shape variations on the EEG/magnetoencephalography (MEG) forward and inverse problems. We build a random head model such that each sample represents the head shape of a different individual and solve the forward problem assuming this random head model, using a polynomial chaos expansion. The random solution of the forward problem is then used to quantify the effect of the geometry when the inverse problem is solved with a standard head model. The results derived with this approach are valid for a continuous family of head models, rather than just for a set of cases. The random model consists of three random surfaces that define layers of different electric conductivity, and we built an example based on a set of 30 deterministic models from adults. Our results show that for a dipolar source model, the effect of the head shape variations on the EEG/MEG inverse problem due to the random head model is slightly larger than the effect of the electronic noise present in the sensors. The variations in the EEG inverse problem solutions are due to the variations in the shape of the volume conductor, while the variations in the MEG inverse problem solutions, larger than the EEG ones, are caused mainly by the variations of the absolute position of the sources in a coordinate system based on anatomical landmarks, in which the magnetometers have a fixed position.     

Multivariate reconstruction of functional networks from cortical sources dynamics in MEG/EEG

In this paper, we present a simple method to find networks of time-correlated brain sources, using a singular value decomposition (SVD) analysis of the source matrix estimated after any linear distributed inverse problem in magnetoencephalography (MEG) and electroencephalography (EEG). Despite the high dimension of the source space, our method allows for the rapid computation of the source matrix. In order to do this, we use the linear relationship between sensors and sources, and show that the SVD can be calculated through a simple and fast computation. We show that this method allows the estimation of one or several global networks of correlated sources without calculating a coupling coefficient between all pairs of sources. A series of simulations studies were performed to estimate the efficiency of the method. In order to illustrate the validity of this approach in experimental conditions, we used real MEG data from a visual stimulation task on one test subject and estimated, in different time windows of interest, functional networks of correlated sources.     

任意方向匀速直线运动模糊的点扩展函数估计

在运动图像复原中,建立图像退化模型的关键是找到准确的点扩展函数(PSF)。提出了一种基于单幅图像的、改进的任意方向匀速直线运动模糊PSF的估计方法。利用基于图像频谱亮线灰度特征的方向鉴别方法鉴别模糊图像的模糊方向,利用微分自相关的方法对模糊图像的模糊尺寸进行计算,通过计算模糊图像沿二维直线运动方向不同距离的重叠度,来计算得到相应的PSF。通过开展仿真分析和成像实验,演示了PSF估计和图像复原过程。通过采用图像质量评价函数,将图像复原结果与现有算法进行对比,验证了所提出方法的有效性。     

基于小波域稀疏最优的图像修复方法

由模糊和噪声引起的图像退化属于非线性病态逆问题,修复比较困难.由于小波的稀疏表示能力较强,为提高修复质量,提出利用正交小波作为稀疏基,以小波系数的稀疏性为先验构造凸函数,最小化后得到修复图像;并提出将优化问题转化为逼近算子形式,利用不动点理论求解;证明了只需对构造出来的迭代形式的解析解反复迭代就可以得到最优解.对方法的构造过程、收敛性和复杂度进行了细致的分析,给出了迭代解,并结合加速方法提高了算法速度.仿真表明,本文方法具有较强的修复能力,收敛速度较快,能够有效去除模糊和噪声,保留图像的边缘和细节信息.     

基于变分贝叶斯估计的相机抖动模糊图像的盲复原算法

在曝光过程中由于相机抖动而导致的运动模糊,是一种常见的图像降质现象。该文提出了一种基于变分贝叶斯估计和自然图像梯度统计特性的盲复原算法,用于恢复相机抖动模糊图像,同时针对图像复原过程中出现的振铃效应,设计了一种基于分区域检测和Fuzzy滤波器的去振铃效应方法。实验结果表明,该文提出的盲复原算法能够有效地去除图像中因相机抖动而产生的模糊,而且在保持图像边缘和细节的同时,可以较好地降低振铃效应对图像复原质量的影响。     

Blind image deconvolution for single motion-blurred image

Motion blur due to camera shake during exposure is one of the most common reasons of image degradation,which usually reduces the quality of photographs seriously.Based on the statistical properties of the natural image's gradient and the blur kernel,a blind deconvolution algorithm is proposed to restore the motion-blurred image caused by camera shake,adopting the variational Bayesian estimation theory.In addition,the ring effect is one problem that is not avoided in the process of image deconvolution,and usually makes the visual effect of the restored image badly.So a dering method is put forward based on the sub-region detection and fuzzy filter.Tested on the real blurred photographs,the experimental results show that the proposed algorithm of blind image deconvolution can remove the camera-shake motion blur from the degraded image effectively,and can eliminate the ring effect better,while preserve the edges and details of the image well.     

Conventional and wavelet coherence applied to sensory-evoked electrical brain activity

The use of coherence is a well-established standard approach for the analysis of biomedical signals. Being entirely based on frequency analysis, i.e., on spectral properties of the signal, it is not possible to obtain any information about the temporal structure of coherence which is useful in the study of brain dynamics, for example. Extending the concept of coherence as a measure of linear dependence between realizations of a random process to the wavelet transform, this paper introduces a new approach to coherence analysis which allows to monitor time-dependent changes in the coherence between electroenecphalographic (EEG) channels. Specifically, we analyzed multichannel EEG data of 26 subjects obtained in an experiment on associative learning, and compare the results of Fourier coherence and wavelet coherence, showing that wavelet coherence detects features that were inaccessible by application of Fourier coherence.     

基于精细化频带的脑磁信号跨频耦合特征分析

脑信号跨频耦合现象与人的意识活动密切相关。本文将精细化的频带划分方法和多种跨频耦合的度量方法相结合,研究不同类型手腕运动的脑磁信号跨频耦合特征。首先对四类手运动脑磁信号进行精细化的频带分解,采用了三种具有主次关系的耦合度量方法（相干、虚部相干、调制指数）,在高频耦合低频、低频耦合高频两种耦合形式下对某一运动方向类型信号显著区别于其它运动方向类型信号的频带数、通道对、频带对进行了统计分析,可以区分出不同方向手腕运动的MEG信号;进一步利用通道间的频带耦合关系以及源频带及其激励通道与响应通道等概念,采用相干耦合度量方法对上述两种耦合形式下的统计结果进行分析,获得相应的源频带、激励通道、响应通道,从而可以更加有效地区分不同方向手腕运动的MEG信号。      

基于时域平滑约束的脑磁时序信号逆问题求解方法

由脑磁时序信号重建脑内时序神经信号时,除了要保证重建信号位置和强度的准确性,还要避免重建源信号在时域上瞬变.针对这一问题,提出了一种基于时域平滑约束的脑磁时序信号逆问题求解方法.该方法不同于传统最小范数估计算法（Minimum Norm Estimate,MNE）,通过引入时域平滑正则算子构造双参数混合正则化,根据广义交叉验证（Generalized Cross-Validation,GCV）原则选取双正则化参数后,根据单正则项的解在源信号中的权重将其进行线性组合估算出源信号.仿真数据实验表明,本文方法比传统MNE方法的总体均方误差小,且各时刻均方误差基本稳定在同一水平;同时本文方法重建的源信号与仿真源信号变化趋势基本一致.真实数据实验发现,本文方法重建结果的曲率变化率为0.0640,而传统MNE方法重建结果的曲率变化率为0.1646.实验结果证明本文方法能重建出空域准确且时域平滑的脑内神经信号.     

Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings

In magnetoencephalography (MEG) and electroencephalography (EEG), independent component analysis is widely applied to separate brain signals from artifact components. A number of different methods have been proposed for the automatic or semiautomatic identification of artifact components. Most of the proposed methods are based on amplitude statistics of the decomposed MEG/EEG signal. We present a fully automated approach based on amplitude and phase statistics of decomposed MEG signals for the isolation of biological artifacts such as ocular, muscle, and cardiac artifacts (CAs). The performance of different artifact identification measures was investigated. In particular, we show that phase statistics is a robust and highly sensitive measure to identify strong and weak components that can be attributed to cardiac activity, whereas a combination of different measures is needed for the identification of artifacts caused by ocular and muscle activity. With the introduction of a rejection performance parameter, we are able to quantify the rejection quality for eye blinks and CAs. We demonstrate in a set of MEG data the good performance of the fully automated procedure for the removal of cardiac, ocular, and muscle artifacts. The new approach allows routine application to clinical measurements with small effect on the brain signal.      

A probabilistic solution to the MEG inverse problem via MCMC methods: the reversible jump and parallel tempering algorithms

We investigated the usefulness of probabilistic Markov chain Monte Carlo (MCMC) methods for solving the magnetoencephalography (MEG) inverse problem, by using an algorithm composed of the combination of two MCMC samplers: Reversible Jump (RJ) and Parallel Tempering (PT). The MEG inverse problem was formulated in a probabilistic Bayesian approach, and we describe how the RJ and PT algorithms are fitted to our application. This approach offers better resolution of the MEG inverse problem even when the number of source dipoles is unknown (RJ), and significant reduction of the probability of erroneous convergence to local modes (PT). First estimates of the accuracy and resolution of our composite algorithm are given from results of simulation studies obtained with an unknown number of sources, and with white and neuromagnetic noise. In contrast to other approaches, MCMC methods do not just give an estimation of a "single best" solution, but they provide confidence interval for the source localization, probability distribution for the number of fitted dipoles, and estimation of other almost equally likely solutions.     

Estimation of coherence between blood flow and spontaneous EEG activity in neonates

Blood flow to the brain responds to changes in neuronal activity and, thus, metabolic demand. In earlier work, we observed correlation between cerebral blood flow and spontaneous electroencephalogram (EEG) activity in neonates. Using coherence, we now found that during Tracé Alternant EEG activity in quiet sleep of normal term neonates, this correlation is strongest at frequencies around 0.1 Hz, reaching statistical significance (p < 0.05) in six of the nine subjects studied (p < 0.07 in eight subjects). Due to noise, artifact, and spontaneous changes in the subjects' EEG patterns, the signals investigated included epochs of missing samples. We, therefore, developed a novel algorithm for the estimation of coherence in such data and applied a Monte Carlo (surrogate data) method for its statistical analysis. This process provides a test for the statistical significance of the maximum coherence within a selected frequency band. In addition to permitting further insight into the mechanisms of cerebral blood flow control, these algorithms are potentially of great benefit in a wide range of biomedical applications, where interrupted (gapped) recordings are often a problem.