Infrared target tracking in multiple feature pseudo-color image with kernel density estimation

Tracking targets in infrared images is a challenging subject due to the low contrast and severe noise. Kernel density estimation (KDE) with robust performance is one of the well-known tracking algorithms. In essence, tracking targets with KDE algorithm is tracking the statistical features of their pixels by the histograms. The universal KDE which can track any features of targets has not been developed. We propose a strategy which does not need to improve on the KDE algorithm itself, but it can make KDE track other features. We first map the features into the pixel intensity and create the feature images. Then these feature images are used to construct the multiple feature pseudo-color images (MFPCIs). The kernel density estimation algorithm tracks targets in MFPCIs can indirectly track these features. Experiments validate that the performance of tracking targets in MFPCIs outperforms that of tracking them in the original infrared images.     

An improved Richardson-Lucy algorithm based on local prior

Ringing is one of the most common disturbing artifacts in image deconvolution. With a totally known kernel, the standard Richardson-Lucy (RL) algorithm succeeds in many motion deblurring processes, but the resulting images still contain visible ringing. When the estimated kernel is different from the real one, the result of the standard RL iterative algorithm will be worse. To suppress the ringing artifacts caused by failures in the blur kernel estimation, this paper improves the RL algorithm based on the local prior. Firstly, the standard deviation of pixels in the local window is computed to find the smooth region and the image gradient in the region is constrained to make its distribution consistent with the deblurring image gradient. Secondly, in order to suppress the ringing near the edge of a rigid body in the image, a new mask was obtained by computing the sharp edge of the image produced using the first step. If the kernel is large-scale, where the foreground is rigid and the background is smoothing, this step could produce a significant inhibitory effect on ringing artifacts. Thirdly, the boundary constraint is strengthened if the boundary is relatively smooth. As a result of the steps above, high-quality deblurred images can be obtained even when the estimated kernels are not perfectly accurate. On the basis of blurred images and the related kernel information taken by the additional hardware, our approach proved to be effective.     

基于多相组重建的航空图像超分辨率算法

为提高航空图像的空间分辨率, 提出一种基于多相组重建的超分辨率算法. 融合图像间的互补信息, 将多帧低分辨率图像作为图像基, 参考帧分解为多相组, 利用差异采样特性构建图像基与参考帧之间的的多相组线性关系重建得到高分辨率图像的多项组, 经图像多相分解逆变换获得融合的高分辨率图像. 根据该融合图像的局部内容和结构信息自适应调整控制核核函数, 应用改进的控制核回归算法去除图像模糊和噪声得到清晰的超分辨率图像. 与传统算法相比, 该算法无需图像配准和迭代过程, 计算效率极大地提高. 实验结果表明, 本文算法能够有效提高航空图像的空间分辨率, 在定量评价指标和主观视觉效果方面都有显著提高.     

A framework for classification with single feature kernel matrix

In this paper, we propose a novel classification framework using single feature kernel matrix. Different from the traditional kernel matrices which make use of the whole features of samples to build the kernel matrix, this research uses features of the same dimension of any two samples to build a sub-kernel matrix and sums up all the sub-kernel matrices to get the single feature kernel matrix. We also use single feature kernel matrix to build a new SVM classifier, and adapt SMO (Sequential Minimal Optimization) algorithm to solve the problem of SVM classifier. The results of the experiments on several artificial datasets and some challenging public cancer datasets display the classification performance of the algorithm. The comparisons between our algorithm and L₂-norm SVM on the cancer datasets demonstrate that the accuracy of our algorithm is higher, and the number of support vectors selected is fewer, indicating that our proposed framework is a more practical approach.     

Two-stage blind deconvolution scheme using useful priors

Hand shake blurry image is a common phenomenon in our daily life. In this paper, a novel blind deconvolution scheme is proposed to recover a single hand shake blurry image. The algorithm is subdivided into two main stages, kernel estimation stage and non-blind deconvolution stage. In the kernel estimation stage, we propose a cost function taking a selected map into consideration. In the non-blind decovolution stage, another cost function is designed using image derivatives prior. We also present an adaptive kernel size selection method instead of traditional manual selection. Extensive experiments on real world blurry images are conducted to demonstrate the performance of our algorithm.     

The combined effect of nonlinear filtration and window size on the accuracy of tissue displacement estimation using detected echo signals

In cardiac elastography, the regional strain and strain rate imaging is based on displacement estimation of tissue sections within the heart muscle carried out with various block-matching techniques (cross-correlation, sum of absolute differences, sum of squared differences, etc.). The accuracy of these techniques depends on a combination of ultrasonic imaging parameters such as ultrasonic frequency of interrogation, signal-to-noise ratio, size of a kernel used in a block-matching algorithm, type of data and speckle decorrelation. In this paper, we discuss the possibility to enhance the accuracy of the displacement estimation via nonlinear filtering of B-mode images before block-matching operation. The combined effect of a filter algorithm and a kernel size on the accuracy of the displacement estimation is analyzed using a 36-frame sequence of grayscale B-mode images of a human heart acquired by an ultrasound system operating at 1.77 MHz. It is shown that the nonlinear filtering of images enables to obtain the desired accuracy (less than one pixel) of the displacement estimation with smaller kernels than without filtering. These results are obtained for two filters--an adaptive anisotropic diffusion filter and a nonlinear Gaussian filter chain.     

Real-time blind deconvolution of retinal images in adaptive optics scanning laser ophthalmoscopy

With the use of adaptive optics (AO), the ocular aberrations can be compensated to get high-resolution image of living human retina. However, the wavefront correction is not perfect due to the wavefront measure error and hardware restrictions. Thus, it is necessary to use a deconvolution algorithm to recover the retinal images. In this paper, a blind deconvolution technique called Incremental Wiener filter is used to restore the adaptive optics confocal scanning laser ophthalmoscope (AOSLO) images. The point-spread function (PSF) measured by wavefront sensor is only used as an initial value of our algorithm. We also realize the Incremental Wiener filter on graphics processing unit (GPU) in real-time. When the image size is 512 × 480 pixels, six iterations of our algorithm only spend about 10 ms. Retinal blood vessels as well as cells in retinal images are restored by our algorithm, and the PSFs are also revised. Retinal images with and without adaptive optics are both restored. The results show that Incremental Wiener filter reduces the noises and improve the image quality.     

非高斯冲激干扰下基于Softplus函数的核自适应滤波算法

针对非高斯环境下一般自适应滤波算法性能严重下降问题,本文提出了一种基于Softplus函数的核分式低次幂自适应滤波算法(kernel fractional lower algorithm based on Softplus function,SP-KFLP),该算法将Softplus函数与核分式低次幂准则相结合,利用输出误差的非线性饱和特性通过随机梯度下降法更新权重.一方面利用Softplus函数的特点在保证了SP-KFLP算法具有良好的抗脉冲干扰性能的同时提高了其收敛速度;另一方面将低次幂误差的倒数作为权重向量更新公式的系数,利用误差突增使得权重向量不更新的方法来抵制冲激噪声,并对其均方收敛性进行了分析.在系统辨识环境下的仿真表明,该算法很好地兼顾了收敛速度和跟踪性能稳定误差的矛盾,在收敛速度和抗脉冲干扰鲁棒性方面优于核最小均方误差算法、核分式低次幂算法和S型核分式低次幂自适应滤波算法.     

Modified spin filter for noise reduction in aerodynamic imaging

A new filtering algorithm was developed for the removal of random noise from digitally imaged aerodynamic flow structures while minimizing geometrical distortion of their shapes. This is a modification of a spin filter previously presented. The algorithm defines a kernel around each pixel and detects the direction of the prevailing pattern passing through that kernel. The median grey level along this preferred direction is then used to replace the grey level recorded by the pixel at the centre of the kernel. Applications of this filter were demonstrated by processing shadowgraph images of shock waves. The noise as measured by the standard deviation from the mean was reduced by nearly 50% after one application of the filter and by nearly 70% after three applications, while distortion of the imaged shock angle remained below the angle measurement uncertainty.     

Moisture gradient vector calculation as a new method for evaluating NMR images of corn (Zea Mays L.) kernels during drying.

Magnetic resonance imaging (MRI) was used to study the moisture migration inside single corn kernel during artificial drying. MRI data were taken every half an hour during drying while the intact kernel was kept inside the magnet. The moisture distribution was not uniform inside the kernel during drying, with the fastest moisture loss being detected in the endosperm, whilst the scutellum maintained the water. Resultant gradient vectors were visualized, as a new evaluation method, from each pixel of the subtraction of segmented MRI images showing the movement of proton density inside the kernel. The data matrix containing the gradient vectors can be stored for latter use as input parameters and to check the mass transfer models.     

Blur identification and restoration of degraded images using generalized cross-validation criterion

In this paper, we describe blur identification and restoration of noisy degraded images. The point-spread function (PSF) can be characterized by the quantity of blur. Thus the blur identification problem can be solved as a parameter estimation problem. The estimation method is a generalized cross-validation (GCV) criterion that is known as a powerful measure that can be used to choose the optimal regularization parameter without a priori knowledge about noise. We use the iterative damped-1east squares (DLS) algorithm which is based on the principle of damped least-squares for restoring noisy degraded images.     

基于多尺度分解的超光谱图像异常检测

提出了一种基于多尺度分解的超光谱图像异常检测算法。在目标和背景均未知的前提下,利用光谱和空间两种信息完成对异常目标信号的定位,从而实现超光谱遥感数据中异常目标检测。首先利用非下采样塔式变换对超光谱图像进行分解,将其划分为不同尺度子块;然后依据超光谱图像同一波段不同尺度空间内相邻系数的相关性,采用不同波段各个尺度空间的反锐化掩模方法优化背景数据分布,从而抑制异常数据对背景的干扰;最后利用设计的核RX算子得到异常目标检测结果。为验证方法的有效性,利用真实和模拟的AVIRIS数据进行了实验,并与经典RX算法相比较,实验结果表明,基于非下采样塔式分解的异常检测方法具有更好的检测性能和较低的虚警。     

A generalized Langevin algorithm for studying permeation across biological ion channels

We present a new leapfrog algorithm for the numerical solution of the generalized Langevin equation (GLE) in the case where the friction kernel is exponentially decaying. Like other leapfrog and Verlet algorithms, our algorithm is second order in velocity and third order in position. It is relatively easy to implement compared with other available algorithms, and would therefore make a good candidate for exploring the effects of finite memory time-scales in situations where modelling the precise functional form of the memory kernel was not important. We have tested this algorithm on a one-dimensional barrier crossing model, and found good asymptotic agreement with limits obtained using Brownian dynamics (BD) simulations, as well as with a theoretical asymptotic limit. We have also used the algorithm to perform a more sophisticated simulation of ion conduction through a KcsA channel. The results are a close match to corresponding results obtained using the Langevin equation, thereby helping to justify the use of Brownian dynamics in KcsA and other similar ion channels.     

基于最小平均距离免疫算法的模糊红外图像分割

由于犯罪分子利用各种方法来避开传统的刑侦图像技术,因而红外图像逐渐成为获取犯罪现场痕迹的有效手段。然而,从犯罪现场拍摄的红外图像其目标痕迹大多是弱化的,所以在这类红外图像中分割目标是一项具有挑战性的任务。已有基于生物免疫的各类算法尚未明确描述免疫分割作用领域,以及免疫网络算法模型中的免疫识别距离。为实现对目标痕迹弱化红外图像的有效分割,提出了一种新的具有免疫作用领域和最小平均免疫识别距离的人工免疫构架,设计了一种具备最小平均距离免疫域的免疫分割算法。该方法根据红外图像的特点,采用多步分类算法、免疫变异和自适应免疫最小均距识别方法,根据目标区域和背景区域的总体统计特性实现最佳分类。实验结果表明,提出的基于最小平均距离的免疫算法能够有效地分割目标弱化的红外图像。与经典的边缘模板和区域模板方法相比,该算法具有更好的分割效果,尤其是针对目标弱化红外图像的分割,该算法能够较好地给出五个手指的边界轮廓。     

基于P范数的核最小对数绝对差自适应滤波算法

为了进一步提高在a稳定分布噪声背景下非线性自适应滤波算法的收敛速度,本文提出了一种新的基于p范数的核最小对数绝对差自适应滤波算法(kernel least logarithm absolute difference algorithm based on p-norm, P-KLLAD).该算法结合核最小对数绝对差算法和p范数,一方面利用最小对数绝对差准则保证了算法在a稳定分布噪声环境下良好的鲁棒性,另一方面在误差的绝对值上添加p范数,通过p范数和一个正常数a来控制算法的陡峭程度,从而提高该算法的收敛速度.在非线性系统辨识和Mackey-Glass混沌时间序列预测的仿真结果表明,本文算法在保证鲁棒性能的同时提高了收敛速度,并且在收敛速度和鲁棒性方面优于核最小均方误差算法、核分式低次幂算法、核最小对数绝对差算法和核最小平均p范数算法.     

A novel, fast entropy-minimization algorithm for bias field correction in MR images

A novel, fast entropy-minimization algorithm for bias field correction in magnetic resonance (MR) images is suggested to correct the intensity inhomogeneity degradation of MR images that has become an increasing problem with the use of phased-array coils. Four important modifications were made to the conventional algorithm: (a) implementation of a modified two-step sampling strategy for stacked 2D image data sets, which included reducing the size of the measured image on each slice with a simple averaging method without changing the number of slices and then using a binary mask generated by a histogram threshold method to define the sampled voxels in the reduced image; (b) improvement of the efficiency of the correction function by using a Legendre polynomial as an orthogonal base function polynomial; (c) use of a nonparametric Parzen window estimator with a Gaussian kernel to calculate the probability density function and Shannon entropy directly from the image data; and (d) performing entropy minimization with a conjugate gradient method. Results showed that this algorithm could correct different types of MR images from different types of coils acquired at different field strengths very efficiently and with decreased computational load.     

核密度估计时-空域滤波红外背景抑制方法

提出一种基于核密度估计的时-空域滤波算法,用于红外搜索跟踪系统图像的背景抑制。算法分为空域滤波和时域滤波两部分。在空域滤波中,采用核密度估计算法对背景进行平滑;在时域滤波中,采用核密度估计算法对经过空域滤波后的图像灰度值进行概率计算,判别属于背景残差的灰度值,然后做进一步的滤除。核方法对背景有很好的光滑性且易于计算机实现,实验表明,这种非参方法设计的时-空域滤波算法对背景杂波有非常良好的抑制效果,信噪比也得到明显提高。     

基于多标签共享子空间学习和内核脊回归的空谱分类算法

针对高光谱图像维度高、地物间非线性可分造成的分类精度低等问题,提出一种基于多标签共享子空间和内核脊回归的空谱分类算法.该算法利用内核脊回归将地物相近像素在线性空间的不可分特征映射到高维空间中,实现分类特性在高维空间下的有效分离,以提高地物相近特性的区分精度;同时将高维样本数据映射到低维共享子空间中,在低维环境下以多类标为指导,引入低秩矩阵建立类别标签与共享空间的预测关系,挖掘多标签间的共同特性,提高融合利用多类别间的共同属性提高高光谱图像的分类精度;最后利用奇异值分解迭代法求解目标函数,一定程度上加速参数求解.在Indian Pines和Pavia University两组高光谱数据集上进行仿真实验,实验结果表明,与其他同类算法相比,在低样本比例下,本文算法在总体分类精度、平均分类精度和Kappa系数等评价指标上至少提高4.76%、4.24%和5.19%,与非内核化的算法相比,本文算法在基本不增加运行时间的情况下总体分类精度、平均分类精度和Kappa系数至少提高2.92%、2.8%和3.48%.     

Algorithm of far-field centre estimation based on phase-only matched filter

Estimation of the far-field centre is carried out in beam auto-alignment. In this paper, the features of the far-field of a square beam are presented. Based on these features, a phase-only matched filter is designed, and the algorithm of centre estimation is developed. Using the simulated images with different kinds of noise and the 40 test images that are taken in sequence, the accuracy of this algorithm is estimated. Results show that the error is no more than one pixel for simulated noise images with a 99% probability, and the stability is restricted within one pixel for test images. Using the improved algorithm, the consumed time is reduced to 0.049s.