Tracking and finite element analysis of stripe deformation in magnetic resonance tagging

Magnetic resonance tissue tagging allows noninvasive in vivo measurement of soft tissue deformation. Planes of magnetic saturation are created, orthogonal to the imaging plane, which form dark lines (stripes) in the image. The authors describe a method for tracking stripe motion in the image plane, and show how this information can be incorporated into a finite element model of the underlying deformation. Human heart data were acquired from several imaging planes in different orientations and were combined using a deformable model of the left ventricle wall. Each tracked stripe point provided information on displacement orthogonal to the original tagging plane, i.e., a one-dimensional (1-D) constraint on the motion. Three-dimensional (3-D) motion and deformation was then reconstructed by fitting the model to the data constraints by linear least squares. The average root mean squared (rms) error between tracked stripe points and predicted model locations was 0.47 mm (n=3,100 points). In order to validate this method and quantify the errors involved, the authors applied it to images of a silicone gel phantom subjected to a known, well-controlled, 3-D deformation. The finite element strains obtained were compared to an analytic model of the deformation known to be accurate in the central axial plane of the phantom. The average rms errors were 6% in both the reconstructed shear strains and 16% in the reconstructed radial normal strain.     

Noise reduction of image sequences using motion compensation andsignal decomposition

In this paper, a new spatio-temporal filtering method for removing noise from image sequences is proposed. This method combines the use of motion compensation and signal decomposition to account for the effects of object motion. Because of object motion, image sequences are temporally nonstationary, which requires the use of adaptive filters. By motion compensating the sequence prior to filtering, nonstationarities, i.e., parts of the signal that are momentarily not stationary, can be reduced significantly. However, since not all nonstationarities can be accounted for by motion, a motion-compensated signal still contains nonstationarities. An adaptive algorithm based on order statistics is described that decomposes the motion-compensated signal into a noise-free nonstationary part and a noisy stationary part. An RLS filter is then used to filter the noise from the stationary signal. Our new method is experimentally compared with various noise filtering approaches from literature.     

彩色PDP基于亮度控制的图像增强技术

开发了一种图像增强技术,通过对图像进行网格化处理,对每个图像网格的亮度进行控制,调整误差扩展算法中的扩散因子.为了确保放电稳定性,对于给定的子场编码,选择能够产生稳定放电的子场排列方式进行显示.考虑到图像亮度分布特性,可以使用多个误差扩散因子选择阈值,设置多组不同的扩散因子,并应用于不同的图像网格区域,进一步增强低灰度级图像的显示能力.     

改进的航空全帧型面阵CCD相机电子式像移补偿方法

目前基于TDI 方式的电子像移补偿方法中曝光期间电荷包以行为步长进行转移,使电荷包移动和像移之间存在较大的非同步效应,降低了补偿效果。为了提高像移补偿效果,首先分析了目前基于TDI 方式的电子像移补偿方法中电荷包移动的离散性对补偿效果的影响,提出了一种改进的电子像移补偿方法,从而可以大大减小电荷包和像移之间存在的非同步效应,对比两种方法调制度,从理论上证明了其对补偿效果的提高作用。给出了改进式的电子式像移补偿方法的驱动时序图,通过室内模拟不同像移补偿实验进行了两种方法的验证,分别比较采用两种像移补偿方法图像的清晰度。结果表明,改进式电子像移补偿法图像调制度平均值47/96大于传统像移补偿法图像调制度平均值1/3,改进式像移补偿法图像清晰度平均值为0.550 2大于传统像移补偿法图像清晰度平均值0.475 3。可以看出,改进式的像移补偿方法相对于传统TDI像移补偿方法,效果明显改善。     

Noise reduction in high dynamic range images

Multiple images with different exposures are used to produce a high dynamic range (HDR) image. Sometimes high-sensitivity setting is needed for capturing images in low light condition as in an indoor room. However, current digital cameras do not produce a high-quality HDR image when noise occurs in low light condition or high-sensitivity setting. In this paper, we propose a noise reduction method in generating HDR images using a set of low dynamic range (LDR) images with different exposures, where ghost artifacts are effectively removed by image registration and local motion information. In high-sensitivity setting, motion information is used in generating a HDR image. We analyze the characteristics of the proposed method and compare the performance of the proposed and existing HDR image generation methods, in which Reinhard et al.’s global tone mapping method is used for displaying the final HDR images. Experiments with several sets of test LDR images with different exposures show that the proposed method gives better performance than existing methods in terms of visual quality and computation time.     

Regenerating MR tagged images using harmonic phase (HARP) methods

Magnetic resonance tagging has proven useful in the visualization and quantification of cardiac motion. Traditionally, tags are designed to have crisp geometric profiles in order to enhance both visualization and detection of tags. Recent image acquisition and analysis methods, however, have been designed to exploit sinusoidal tag profiles. This paper presents a method based on harmonic phase (HARP) concepts to synthesize tag lines that have both crisp profiles and alternative orientations from the original sinusoidal patterns. Results are demonstrated on images acquired with SPAMM, CSPAMM, and fast-HARP pulse sequences.     

Manoeuvring target motion parameter estimation for ISAR image fusion

In inverse synthetic aperture radar (ISAR) imaging applications, generating an image of a manoeuvring target is a challenge due to the tradeoff between the fine image resolution requirements and the temporal variability of the Doppler response. The time frequency transform (TFT) is a common method to solve this problem. Prior TFT-based research assumes that the ISAR observes the target from a single fixed aspect. Because of its manoeuvring characteristics, the target motion features are sometimes not extractable from the observing aspect, but become much more distinct from other aspects. ISAR image fusion is thus able to provide significant advantages, since multiple looks of the same target from different aspects will increase the available knowledge. After constructing the common projection plane and building up the physical connection between radars and the manoeuvring target, the parameters that describe the target motion are estimated for very short time duration within which the Doppler response can be assumed to be a constant. Simulation results confirm the effectiveness of the estimation procedure and thus the fusion method. The authors conclude that the ISAR system must be carefully configured in order to realise the full benefits of their image fusion technique for manoeuvring targets.     

Resolution enhancement of monochrome and color video using motioncompensation

We propose an iterative algorithm for enhancing the resolution of monochrome and color image sequences. Various approaches toward motion estimation are investigated and compared. Improving the spatial resolution of an image sequence critically depends upon the accuracy of the motion estimator. The problem is complicated by the fact that the motion field is prone to significant errors since the original high-resolution images are not available. Improved motion estimates may be obtained by using a more robust and accurate motion estimator, such as a pel-recursive scheme instead of block matching, in processing color image sequences, there is the added advantage of having more flexibility in how the final motion estimates are obtained, and further improvement in the accuracy of the motion field is therefore possible. This is because there are three different intensity fields (channels) conveying the same motion information. In this paper, the choice of which motion estimator to use versus how the final estimates are obtained is weighed to see which issue is more critical in improving the estimated high-resolution sequences. Toward this end, an iterative algorithm is proposed, and two sets of experiments are presented. First, several different experiments using the same motion estimator but three different data fusion approaches to merge the individual motion fields were performed. Second, estimated high-resolution images using the block matching estimator were compared to those obtained by employing a pel-recursive scheme. Experiments were performed on a real color image sequence, and performance was measured by the peak signal to noise ratio (PSNR).     

Identification and restoration of spatially variant motion blurs insequential images

Sequential imaging cameras are designed to record objects in motion. When the speed of the objects exceeds the temporal resolution of the shutter, the image is blurred. Because objects in a scene are often moving in different directions at different speeds, the degradation of a recorded image may be characterized by a space-variant point spread function (PSF). The sequential nature of such images can be used to determine the relative motion of various parts of the image. This information can be used to estimate the space-variant PSF. A modification of the Landweber iteration is developed to utilize the space-variant PSF to produce an estimate of the original image.     

基于运动估计的模糊图像盲复原

针对由于物体在不同方向移动速度不同导致退化的CCD图像空间移变特性的运动模糊,提出一种基于隔行扫描空间移变特性的CCD图像恢复方案。方法主要依据隔行扫描图像分为上下两场的特性,借助插值算法将单幅图像按场拆分为两幅存在时间差的图像,然后引入视频处理中基于图像序列确定运动向量的块匹配算法,估计模糊参数,从而实现应用图像序列算法估计单幅图像点扩散函数,最后利用最小二成方算法进行复原,其中引入了图像定向信息测量操作保持边缘。仿真实验结果表明,本文算法比以往算法具有大的信噪比(SNR)和图像保真度(IMFID),以及小的平均绝对差(ABSDIFF)和均方误差(MSE)。     

一种鲁棒性强的电子稳像方法

介绍了电子稳像的原理、分类和实现方法,针对稳像过程中的主要问题——提高算法适应性和鲁棒性进行了研究,提出一种新的基于改进MCD法求解运动矢量的电子稳像方法,首先对图像进行梯度转换,再利用梯度图像进行MCD相关匹配。实验证明,此方法有效地克服了由于异物进入时的算法不可靠的问题,提高了算法的鲁棒性和适应性。     

A respiratory motion artifact reduction method in magnetic resonance imaging of the chest

An image reconstruction algorithm based on the assumption that the respiratory motion of the chest is linear in space and arbitrary in time is presented. The linear respiratory motion causes phase distortion on the magnetic resonance (MR) data. As a result of this motion, the MR data are the samples of the Fourier transform of the spin density on a nonrectangular grid. In image reconstruction. before taking the inverse Fourier transform, the phase distortion is compensated for, and the rectangular samples are interpolated from the existing nonrectangular samples. A significant amount of motion artifact suppression is achieved with a rough knowledge on the motion using this method. It is demonstrated that the respiratory motion model parameters can be estimated using the information hidden in the motion artifacts.     

深度学习驱动的智能电网运行图像数据压缩技术

随着智能电网的快速发展,用于监视电网运行状况的测量设备大规模投入,其产生的海量运行图像等监视数据由于规模大、维度高、数据冗余等问题难以得到有效利用。为了进一步提高电力大数据的分析应用能力,文中提出一种基于深度学习的电网运行图像数据压缩方法,考虑电网图像监视数据在时序上的耦合关联,通过卷积神经网络对电网运行图像数据进行压缩,有效减少了电网运行图像数据的冗余度。与其他方法相比,基于卷积神经网络的图像数据压缩模型不依赖于人工的数据特征提取和工程经验,可以直接以电网中采集到的原始图像数据的灰度函数作为模型的输入,将数据的特征提取和分类合二为一,实现电网运行图像数据的高效、便捷压缩。通过仿真进行了文中所提方法有效性的验证,结果表明,与其他神经网络相比,所提方法在电网图像压缩效率及压缩精度中具有较强优势。     

Fully constrained least squares linear spectral mixture analysismethod for material quantification in hyperspectral imagery

Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sum-to-one constraint and the abundance nonnegativity constraint. The first constraint requires the sum of the abundance fractions of materials present in an image pixel to be one and the second imposes a constraint that these abundance fractions be nonnegative. While the first constraint is easy to deal with, the second constraint is difficult to implement since it results in a set of inequalities and can only be solved by numerical methods. Consequently, most LSMA-based methods are unconstrained and produce solutions that do not necessarily reflect the true abundance fractions of materials. In this case, they can only be used for the purposes of material detection, discrimination, and classification, but not for material quantification. The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification. Since no closed form can be derived for this method, an efficient algorithm is developed to yield optimal solutions. In order to further apply the designed algorithm to unknown image scenes, an unsupervised least squares error (LSE)-based method is also proposed to extend the FCLS method in an unsupervised manner     

3-D Kalman filter for image motion estimation

This paper presents a new three-dimensional (3-D) Markov model for motion vector fields. The three dimensions consist of the two space dimensions plus a scale dimension. We use a compound signal model to handle motion discontinuity in this 3-D Markov random field (MRF). For motion estimation, we use an extended Kalman filter as a pel-recursive estimator. Since a single observation can be sensitive to local image characteristics, especially when the model is not accurate, we employ windowed multiple observations at each pixel to increase accuracy. These multiple observations employ different weighting values for each observation, since the uncertainty in each observation is different. Finally, we compare this 3-D model with earlier proposed one-dimensional (1-D) (coarse-to-fine scale) and two-dimensional (2D) spatial compound models, in terms of motion estimation performance on a synthetic and a real image sequence.     

高分辨率空间相机俯仰成像的像移补偿方法

为了使高分率空间TDI（Time Delay and Integration） CCD相机适应卫星大俯仰姿态角,以增强空间遥感的时间分辨率,分析了卫星大俯仰姿态对高分辨空间相机成像的影响,使用光学投影方法,定性分析了俯仰成像时不同视场处的像移速度差异;根据TDI CCD工作方式,提出了横向像移图像生成原理,建立了横向像移图像的恢复模型,并完成了仿真验证和算法精度分析。分析结果表明:卫星俯仰姿态越大时,像面不同视场处横向像移速度差异增大;从仿真实验和精度分析可以看到俯仰成像时采用横向像移图像恢复方法可以得到无横向像移的图像,横向像移图像采集位数越高,恢复后的图像灰度值误差越小。横向像移图像恢复方法增强了空间相机对卫星大俯仰姿态的适应能力,极大地提高TDI CCD空间相机敏捷成像能力。     

基于自适应补偿的电子稳像方法

基于车载的光电系统在采集图像的过程中,载车的随机振动会导致视频序列发生抖动,需要通过稳像技术来消除。为解决传统电子稳像方法中存在帧间跳变、机械性选择参考帧,以及边缘信息丢失的问题,提出了一种基于自适应补偿的电子稳像方法。首先,自适应地更新参考帧,后续帧与更新的参考帧进行特征点的提取和匹配,并通过参数迭代方法求解相对于第1帧的全局运动参数;然后,区分不同的全局运动参数:旋转角度、水平、垂直方向的偏移量,并通过运动滤波从全局运动参数中分离出扫描运动,补偿随机抖动;最后,采用后向映射方式实现运动补偿,并运用图像镶嵌,即利用参考基准帧的信息对校正后图像的边缘信息进行补偿。实验结果表明:稳像后视频序列有很好的视觉稳定效果,且提高了峰值信噪比(PSNR)和帧间变换保真度(ITF)。本文的方法具有很好的鲁棒性,在不同的视频场景下都可以实现稳像,可以满足稳像的精度要求。     

Image segmentation and selective smoothing by using Mumford-Shah model.

Recently, Chan and Vese developed an active contour model for image segmentation and smoothing by using piecewise constant and smooth representation of an image. Tsai et al. also independently developed a segmentation and smoothing method similar to the Chan and Vese piecewise smooth approach. These models are active contours based on the Mumford-Shah variational approach and the level-set method. In this paper, we develop a new hierarchical method which has many advantages compared to the Chan and Vese multiphase active contour models. First, unlike previous works, the curve evolution partial differential equations (PDEs) for different level-set functions are decoupled. Each curve evolution PDE is the equation of motion of just one level-set function, and different level-set equations of motion are solved in a hierarchy. This decoupling of the motion equations of the level-set functions speeds up the segmentation process significantly. Second, because of the coupling of the curve evolution equations associated with different level-set functions, the initialization of the level sets in Chan and Vese's method is difficult to handle. In fact, different initial conditions may produce completely different results. The hierarchical method proposed in this paper can avoid the problem due to the choice of initial conditions. Third, in this paper, we use the diffusion equation for denoising. This method, therefore, can deal with very noisy images. In general, our method is fast, flexible, not sensitive to the choice of initial conditions, and produces very good results.     

基于联合相机路径的红外视频稳像算法

针对由于视差变化而导致的红外视频稳像技术难题,文中提出了一种基于联合相机路径的红外视频稳像算法,该算法主要包括:预处理、联合相机路径求解、多路径优化、运动补偿四个步骤。首先,要对红外图像进行直方图均衡化、特征点提取和匹配处理、预映射。接着,要将每帧图像分为m×n个网格单元,利用基于网格的映射运动表征,将每一帧中对应网格得到的局部单应性矩阵进行相乘得到联合相机路径。然后,对联合相机路径采用“多路径优化”策略进行平滑处理。最后,利用平滑后路径进行运动补偿稳定视频。实验结果表明,该方法能有效地处理由于视差而引起的非线性运动,相比传统的稳像算法效果更好,当特征点存在部分遮挡时,也能取得不错的稳像效果。     

No-reference assessment of blur and noise impacts on image quality

The quality of images may be severely degraded in various situations such as imaging during motion, sensing through a diffusive medium, and low signal to noise. Often in such cases, the ideal un-degraded image is not available (no reference exists). This paper overviews past methods that dealt with no-reference (NR) image quality assessment, and then proposes a new NR method for the identification of image distortions and quantification of their impacts on image quality. The proposed method considers both noise and blur distortion types that may exist in the image. The same methodology employed in the spatial frequency domain is used to evaluate both distortion impacts on image quality, while noise power is further independently estimated in the spatial domain. Specific distortions addressed here include additive white noise, Gaussian blur and de-focus blur. Estimation results are compared to the true distortion quantities, over a set of 75 different images.