Lifting-based wavelet domain adaptive Wiener filter for image enhancement

A method of applying lifting-based wavelet domain Wiener filter (LBWDMF) in image enhancement is proposed. Lifting schemes have emerged as a powerful method for implementing biorthogonal wavelet filters. They exploit the similarity of the filter coefficients between the low-pass and high-pass filters to provide a higher speed of execution, compared to classical wavelet transforms. LBWDMF not only helps in reducing the number of computations but also achieves lossy to lossless performance with finite precision. The proposed method utilises the multi-scale characteristics of the wavelet transform and the local statistics of each subband. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters and then applies a Wiener filter in the wavelet domain and finally transforms the result into the spatial domain. When the peak signal-to-noise ratio (PSNR) is low, transforming an image to the lifting-based wavelet domain and applying the Wiener filter in the wavelet domain produces better results than directly applying Wiener filter in spatial domain. In other words each subband is processed independently in the wavelet domain by a Wiener filter. Moreover, in order to validate the effectiveness of the proposed method the result obtained using the proposed method is compared to those using the spatial domain Wiener filter (SDWF) and classical wavelet domain Wiener filter (CWDWF). Experimental results show that the proposed method has better performance over SDWF and CWDWF both visually and in terms of PSNR.     

A new spatiotemporal filtering method for single-trial estimation of correlated ERP subcomponents

A novel spatiotemporal filtering method for single trial estimation of event-related potential (ERP) subcomponents is proposed here. Unlike some previous works in ERP estimation [1], , the proposed method is able to estimate temporally correlated ERP subcomponents such as P3a and P3b. A new cost function is, therefore, defined which can deflate one of the correlated subcomponents. The method is applied to both simulated and real data and has shown to perform very well even in low signal-to-noise ratio situations. In addition, the method is compared to spatial principal component analysis and its superiority has been confirmed by using simulated signals. The approach can be especially useful in mental fatigue analysis where the relative variability of P300 subcomponents is the key factor in detecting the level of fatigue.     

Small Target Detection Using Bilateral Filter Based on Edge Component

Bilateral filter (BF) performs sharpness enhancement and noise removal by using two Gaussian filters, the domain filter in spatial domain and the range filter in intensity domain. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF for small target detection with the adaptive standard deviation based on the analysis of the edge component, also having the variable filter size. At first, threshold of pixel edge components for four directions decides whether any pixel is potential small targets or not. For the proposed BF operation for the potential small target pixels, its edge component is mapped to two standard deviations of the domain filter and the range filter in the proposed BF by mapping function. When the BF comes to a target region, the two standard deviations increase in proportion to the edge component to blur the small targets. To further blur the small targets, the filter size of the BF also increases by its edge component. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is more robust and efficient than the conventional methods.     

Enhancement of Event Related Potentials by Iterative Restoration Algorithms

An iterative procedure for the restoration of event related potentials (ERP) is proposed and implemented. The method makes use of assumed or measured statistical information about latency variations in the individual ERP components. The signal model used for the restoration algorithm consists of a time-varying linear distortion and a positivity/negativity constraint. Additional preprocessing in the form of low-pass filtering is needed in order to mitigate the effects of additive noise. Numerical results obtained with real data show clearly the presence of enhanced and regenerated components in the restored ERP's. The procedure is easy to implement which makes it convenient when compared to other proposed techniques for the restoration of ERP signals.     

Image Restoration by Lifting‐Based Wavelet Domain E‐Median Filter

In this paper, we propose a method of applying a lifting‐based wavelet domain e‐median filter (LBWDEMF) for image restoration. LBWDEMF helps in reducing the number of computations. An e‐median filter is a type of modified median filter that processes each pixel of the output of a standard median filter in a binary manner, keeping the output of the median filter unchanged or replacing it with the original pixel value. Binary decision‐making is controlled by comparing the absolute difference of the median filter output and the original image to a preset threshold. In addition, the advantage of LBWDEMF is that probabilities of encountering root images are spread over sub‐band images, and therefore the e‐median filter is unlikely to encounter root images at an early stage of iterations and generates a better result as iteration increases. The proposed method transforms an image into the wavelet domain using lifting‐based wavelet filters, then applies an e‐median filter in the wavelet domain, transforms the result into the spatial domain, and finally goes through one spatial domain e‐median filter to produce the final restored image. Moreover, in order to validate the effectiveness of the proposed method we compare the result obtained using the proposed method to those using a spatial domain median filter (SDMF), spatial domain e‐median filter (SDEMF), and wavelet thresholding method. Experimental results show that the proposed method is superior to SDMF, SDEMF, and wavelet thresholding in terms of image restoration.     

一种基于相位敏感光时域反射计的多参量振动传感器

提出了一种基于数字相干检测和维纳滤波技术的相位敏感光时域反射计(φ-OTDR)。在该技术中,实时解调出了沿光纤链路散射回的瑞利信号电场的振幅和相位。通过分析解调出的振幅和相位,检测到了位于3.5km传感光纤上的一个由压电陶瓷(PZT)所产生的正弦振动信号的位置、频率和强度。应用维纳滤波降低了由激光器的相位噪声和探测器加性噪声引起的相位波动。系统的空间分辨率为5m。     

T2 restoration and noise suppression of hybrid MR images using Wiener and linear prediction techniques

The authors address the problem of enhancing hybrid magnetic resonance (MR) images degraded by T2 effects and additive measurement noise. To reduce imaging time, MR signals are acquired using hybrid imaging (HI) sequences such as rapid acquisition relaxation-enhanced (RARE) and fast spin-echo (FSE). With these techniques, T2 effects act as a distortion filter. This T2 filter affects the signal and results in image spatial resolution and/or contrast loss. Furthermore, the amplitude and phase discontinuities in the T2 filter frequency response function may generate serious ringing artifacts. These distortions will damage image quality and affect object detectability. The authors use the Wiener filter and linear prediction (LP) technique to process HI MR signals in the spatial frequency domain (K-space) and the hybrid domain, respectively. Based on the average amplitude symmetry constraint of the spin echo signal, the amplitude frequency response function of the T2 distortion filter can be estimated and used in the Wiener filter for a global T2 amplitude restoration. Then, the linear prediction technique is utilized to obtain the local signal amplitude and phase estimates around the discontinuities of the frequency response function of the T2 filter. These estimates are used to make local amplitude and phase corrections. The effectiveness of this combined technique in correcting T2 distortion and reducing the measurement noise is analyzed and demonstrated using experiments on both phantoms and human studies.     

A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG's

The proposed filter assumes the noisy electrocardiography (ECG) to be modeled as a signal of deterministic nature, corrupted by additive muscle noise artefact. The muscle noise component is treated to be stationary with known second-order characteristics. Since noise-free ECG is shown to possess a narrow-band structure in discrete cosine transform (DCT) domain and the second-order statistical properties of the additive noise component is preserved due to the orthogonality property of DCT, noise abatement is easily accomplished via subspace decomposition in the transform domain. The subspace decomposition is performed using singular value decomposition (SVD). The order of the transform domain SVD filter required to achieve the desired degree of noise abatement is compared to that of a suboptimal Wiener filter using DCT. Since the Wiener filter assumes both the signal and noise structures to be statistical, with a priori known second-order characteristics, it yields a biased estimate of the ECG beat as compared to the SVD filter for a given value of mean-square error (mse). The filter order required for performing the subspace smoothing is shown to exceed a certain minimal value for which the mse profile of the SVD filter follows the minimum-mean-quare error (mmse) performance warranted by the suboptimal Wiener filter. The effective filter order required for reproducing clinically significant features in the noisy ECG is then set by an upper bound derived by means of a finite precision linear perturbation model. A significant advantage resulting from the application of the proposed SVD filter lies in its ability to perform noise suppression independently on a single lead ECG record with only a limited number of data samples.     

基于注意力学习的正则化相关滤波跟踪算法

边界效应是制约相关滤波跟踪性能的一个重要因素.目前大多数方法只是简单地采用先验知识,如逆高斯分布,预设掩模等,或者分割前景目标作为正则化项,进行约束求解,并没有考虑目标的空时域特性.针对这一问题,本文提出一种基于注意力学习的正则化相关滤波跟踪算法.该方法考虑目标在空间中的分布特性,利用注意力机制学习目标的特定空间权重,适应目标在空域中的变化;同时利用目标在时域中的连续性,通过对注意力权重矩阵的约束来间接调整滤波器;最后通过交替方向乘子（ADMM）算法迭代优化模型.我们在标准的数据库上进行大量实验,结果表明本文算法能实时跟踪目标,并且在精确度和成功率上都有了一定的提升.     

利用空域滤波虚光栅叠栅法提取干涉图波面

虚光栅叠栅条纹法是一种利用单幅干涉图提取波面信息的方法,为了解决叠栅条纹的滤波问题,提出了一种基于高斯函数的空域滤波法。利用高斯函数在空域中对叠栅条纹图进行模糊处理,滤除不需要的高频分量,仅保留包含波面相位信息的低频分量。重点研究了高斯函数滤波窗口的选择和干涉图的载频之间的对应关系。该方法具有计算量小、易于选取滤波窗口的优点。对一光学平面的面形测量结果表明,利用空域滤波虚光栅叠栅法提取的波面[峰谷(PV)值为0.080λ,均方根(RMS)值为0.020λ,λ=632.8nm]与利用Zygo GPI干涉仪的四步移相法得到的波面(PV值为0.079λ,RMS值为0.017λ)相吻合。     

Common spatio-temporal pattern for single-trial detection of event-related potential in rapid serial visual presentation triage

Searching for target images in large volume imagery is a challenging problem and the rapid serial visual presentation (RSVP) triage is potentially a promising solution to the problem. RSVP triage is essentially a cortically-coupled computer vision technique that relies on single-trial detection of event-related potentials (ERP). In RSVP triage, images are shown to a subject in a rapid serial sequence. When a target image is seen by the subject, unique ERP characterized by P300 are elicited. Thus, in RSVP triage, accurate detection of such distinct ERP allows for fast searching of target images in large volume imagery. The accuracy of the distinct ERP detection in RSVP triage depends on the feature extraction method, for which the common spatial pattern analysis (CSP) was used with limited success. This paper presents a novel feature extraction method, termed common spatio-temporal pattern (CSTP), which is critical for robust single-trial detection of ERP. Unlike the conventional CSP, whereby only spatial patterns of ERP are considered, the present proposed method exploits spatial and temporal patterns of ERP separately, providing complementary spatial and temporal features for high accurate single-trial ERP detection. Numerical study using data collected from 20 subjects in RSVP triage experiments demonstrates that the proposed method offers significant performance improvement over the conventional CSP method (corrected p-value < 0.05, Pearson r = 0.64) and other competing methods in the literature. This paper further shows that the main idea of CSTP can be easily applied to other methods.     

虚拟现实视觉体验对事件相关电位影响的研究

虚拟现实技术具有广阔应用前景,但它对大脑信息处理及认知影响尚不清楚。本文结合头皮脑电,设计了平面和虚拟现实两种模式的视觉任务,对比分析虚拟现实对事件相关电位（ERP）的影响,探索沉浸式视觉体验过程中大脑的认知加工过程。参考电极的选择是研究事件相关电位的关键,为获得更客观的脑电信息,本文首先使用参考电极标准化技术将记录的脑电信号的参考近似转换为理想的零点,经过必要的预处理,再采用叠加平均的方法从中提取ERP。结果发现,与平面模式相比,在虚拟现实模式下ERP成分中没有出现显著的P300成分,这可能与大脑产生疲劳感相关;虚拟现实模式下N100成分的潜伏期提前、幅值增大,反映了虚拟现实环境更容易引起注意,并使人产生沉浸感。      

GPS空时抗干扰中一种改进的MWF算法的应用

在GPS接收机的抗干扰抑制中,较单纯时域、频域或空域滤波方法,空时联合处理利用了更多的接收数据信息,因而可以获得更好的干扰抑制性能。针对空时处理方法计算量大的缺陷,文中通过降维处理思想的分析,给出了一种对MWF的前向递推过程进行简化的方法,以此来降低运算量,缩短运算所需的时间,并将这种方法与一般方法进行降秩性能和抗干扰...     

Face recognition under large illumination variations using homomorphic filtering in spatial domain

This paper proposes a homomorphic filtering in spatial domain for reducing of illumination effects in face recognition systems. Also, in this research a simple kernel of homomorphic filter is proposed. Application of this method causes considerable reduction in computational time in the preprocessing step. When a new face image with an arbitrary illumination is given, the homomorphic filter is applied and its reflectance component is extracted. Then the reflectance component is divided into several local regions and histograms of each local region are extracted using multi-resolution uniform local Gabor binary patterns (MULGBP). These histograms are combined for obtaining the overall histogram of the images. Finally, for face recognition, a simple histogram matching process is performed between new face image histogram and the gallery images histogram. The results show that the proposed method is robust for large illumination variation with a reasonable computational complexity.     

Source localization of event-related potentials incorporating spatial notch filters

A novel algorithm for the localization of event-related potential (ERP) sources within the brain is proposed here. In this technique, spatial notch filters are developed to exploit the multichannel electroencephalogram data together with a model of ERP with variable parameters in order to accurately localize the corresponding ERP signal sources. The algorithm is robust in the presence of reasonably high noise. The performance of the proposed system has been compared to that of linear constrained minimum variance (LCMV) beamformer for different noise and correlation levels and its superiority has been demonstrated.      

幅相误差下基于CMCAP-JDL的低空风切变风速估计

针对机载气象雷达存在幅相误差的情况下，导致杂波抑制性能下降，低空风切变风速估计结果不准确的问题，本文提出了一种基于组合空时主通道-局域联合空时自适应处理（Combined Space-time Main Channel Adaptive Processing, CMCAP-JDL）低空风切变风速估计方法。该方法首先对雷达回波数据进行距离依赖性矫正及杂波协方差矩阵估计；然后对空域和时域加权，形成空域主波束和时域主通道，同时增加多个局域处理区域参与联合处理的方式来构造自适应降维处理器；最后使用权矢量对降维后的回波数据实现自适应滤波，完成幅相误差情况下风场速度的准确估计。仿真结果表明，该方法能够在幅相误差的情况下，实现低空风切变风速的准确估计。      

Φ-OTDR光纤传感电缆防外破监测数据预处理方法

针对Φ-OTDR光纤传感电缆外破隐患监测原始数据信噪比低的问题,文章介绍了一种空间域去趋势化与时间域改进谱减法去噪的数据预处理方法。提取光功率距离衰减趋势基线,去除原始数据空间域的衰减趋势,消除Φ-OTDR系统光功率因距离衰减对信号幅度造成的影响,然后采用改进谱减法对时域进行去噪。所提方法应用于现场实测的信号进行了检验,分析结果表明:去趋势化和改进谱减法可有效提高外破监测数据的信噪比,且两者具有叠加效果;现场破碎机、挖掘机、打桩机信号预处理后信噪比分别提高了2.25 dB、6.11 dB、4.72 dB。该方法可为外破监测后续的定位与识别、减少误报率提供参考。     

一种幅度信息辅助多伯努利滤波算法

在许多多目标跟踪场景中,目标返回的幅度通常强于虚警杂波返回的幅度。通过建立更加准确的包含幅度信息的目标和虚警杂波似然函数,可提高多目标估计精度。该文提出一种基于随机有限集的幅度信息辅助多伯努利滤波(Amplitude Information Assistant Multi-Bernoulli Filter, AIA-MBerF)算法。该算法通过建立幅度似然函数将幅度信息引入到多伯努利滤波的更新过程中,并给出针对线性和非线性模型的高斯混合(Gaussian Mixture, GM)和序贯蒙特卡洛(Sequential Monte Carlo, SMC)实现方法。仿真结果表明,该滤波算法相比于传统多伯努利滤波(Multi-Bernoulli Filter, MBerF)无论GM还是SMC实现都可获得更加准确稳定的目标数和对应的目标状态估计。     

基于图稀疏正则化多测量向量模型的高光谱压缩感知重建

压缩感知重建是解决高光谱现有成像模式数据量大冗余度高问题的一个有效机制。针对高光谱图像的多通道特性,该文建立了高光谱压缩感知的多测量向量模型,编码端使用随机卷积算子对各通道进行快速采样,生成测量向量矩阵。解码端构建图稀疏正则化的联合重建模型,在稀疏变换域将高光谱图像分解为谱间的关联成分和差异成分,通过图结构化稀疏度量表征关联成分的空谱相关性,并约束谱间差异成分的稀疏性。进一步提出模型求解的交替方向乘子迭代算法,通过引入辅助变量与线性化技巧,使得每一子问题均存在解析解,降低了模型求解的复杂度。对多个实测数据集进行了对比实验,实验结果验证了该文模型与算法的有效性。