Orientationally polarized dependence of image contrast in doped lithium niobate crystals

In lithium niobate crystals doped with photorefractive iron and copper admixtures when lightening objects with broadband incoherent radiation one can observe typical orientation and polarized dependences of image contrast. The record does not realize at orientation of light bands in the image parallel to the polar axis of the crystal.     

Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems

Most of the techniques developed for infrared (IR) image enhancement (IE) depend heavily on the scene, environmental conditions, and the properties of the imaging system. So, with a set of predefined scenario properties, a content-based IR-IE technique can be developed for better situational awareness. This study proposes an adaptive IR-IE technique based on clustering of wavelet coefficients of an image for sea surveillance systems. Discrete wavelet transform (DWT) of an image is computed and feature vectors are constructed from subband images. Clustering operation is applied to group similar feature vectors that belong to different scene components such as target or background. Depending on the feature vectors, a weight is assigned to each cluster and these weights are used to compute gain matrices which are used to multiply wavelet coefficients for the enhancement of the original image. Enhancement results are presented and a comparison of the performance of the proposed algorithm is given through subjective tests with other well known frequency and histogram based enhancement techniques. The proposed algorithm outperforms previous ones in the truthfulness, detail visibility of the target, artificiality, and total quality criteria, while providing an acceptable computational load.     

Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules

Infrared polarization and intensity imagery provide complementary and discriminative information in image understanding and interpretation. In this paper, a novel fusion method is proposed by effectively merging the information with various combination rules. It makes use of both low-frequency and high-frequency images components from support value transform (SVT), and applies fuzzy logic in the combination process. Images (both infrared polarization and intensity images) to be fused are firstly decomposed into low-frequency component images and support value image sequences by the SVT. Then the low-frequency component images are combined using a fuzzy combination rule blending three sub-combination methods of (1) region feature maximum, (2) region feature weighting average, and (3) pixel value maximum; and the support value image sequences are merged using a fuzzy combination rule fusing two sub-combination methods of (1) pixel energy maximum and (2) region feature weighting. With the variables of two newly defined features, i.e. the low-frequency difference feature for low-frequency component images and the support-value difference feature for support value image sequences, trapezoidal membership functions are proposed and developed in tuning the fuzzy fusion process. Finally the fused image is obtained by inverse SVT operations. Experimental results of visual inspection and quantitative evaluation both indicate the superiority of the proposed method to its counterparts in image fusion of infrared polarization and intensity images.     

Non-contact evaluation of the resonant frequency of a microstructure using ultrasonic wave

This paper presents a novel non-contact method for evaluating the resonant frequency of a microstructure, Firstly, the microstructure under test is excited by ultrasonic waves. This excitation method does not impose any undefined load on the specimen like the electrostatic excitation and also this is the first actual use of ultrasonic wave for exciting a microstructure in the literature. Secondly, the amplitudes of the microstructure are determined by image edge detection using a Mexican hat wavelet transform on the vibrating images of the microstructure. The vibrating images are captured by a CCD camera when the microstructure is vibrated by ultrasonic waves at a series of discrete high frequencies （〉30 kHz）. Upon processing the vibrating images, the amplitudes at various excitation frequencies are obtained and an amplitude-frequency spectrum is obtained from which the resonant frequency is subsequently evaluated. A micro silicon structure consisting of a perforated plate （192 × 192 μm） and two cantilever beams （76 × 43 μm） which is about 4 μm thickness is tested. Since laser interferometry is not required, thermal effects on a test object can be avoided. Hence, the setup is relatively simple. Results show that the proposed method is a simple and effective approach for evaluating the dynamic characteristics of microstructures.     

“天光一号”平滑化角多路系统的建立

 采用诱导空间非相干（FEISI）的平滑方法结合传递傅里叶面的技术，建立了一套平滑化六束角多路系统。该系统输出总能量158 J，能量稳定度约4％，输出脉冲宽度25 ns，靶面有效焦斑直径400 μm，焦斑均匀性1.6％，激光功率密度3.7×10¹² W/cm²。     

单向复合纤维材料弹性系数的超声测量

本文将单向复合纤维材料视作六角对称晶体，认为其弹性特征由五个独立的弹性系数来描述。利用Christoffel方程，我们得到这一各向异性介质中不同方向的声波速度与弹性系数的关系．我们还建立了一套计算机控制的实验测量系统，通过它对纤维板样品中的声速进行测量，反演得到了五个独立的弹性系数C11、C33、C13、C14和C12。理论计算得到的声速和实验测量值吻合较好，表明我们的方法是有实用意义的。     

噪音对光学图象浑沌相位列阵加密和解密的影响

本文提出了一种用浑沌相位列阵对光学图象加密和解密的方法,并重点分析了加法高斯白噪音对光学图象浑沌相位列阵加密和解密的影响.通过计算机模拟发现加密后的图象与原图象相比,抗振幅噪音的能力增强,而抗相位噪音的能力下降.在一定信噪比范围内,相位噪音是影响解密图象质量的主要因素.     

Improving motion estimation by accounting for local image distortion

Cardiac elastography is a useful diagnostic technique for detection of heart function abnormalities, based on analysis of echocardiograms. The analysis of the regional heart motion allows assessing the extent of myocardial ischemia and infarction. In this paper, a new two-stage algorithm for cardiac motion estimation is proposed, where the data is taken from a sequence of 2D echocardiograms. The method combines the advantages of block-matching and optical flow techniques. The first stage employs a standard block-matching algorithm (sum of absolute differences) to provide a displacement estimate with accuracy of up to one pixel. At the second stage, this estimate is corrected by estimating the parameters of a local image transform within a test window. The parameters of the image transform are estimated in the least-square sense. In order to account for typical heart motions, like contraction/expansion, translation and rotation, a local affine model is assumed within the test window. The accuracy of the new algorithm is evaluated using a sequence of 500 grayscale B-mode images, which are generated as distorted, but known copies of an original ROI, taken from a real echocardiogram. The accuracy of the motion estimation is expressed in terms of errors: maximum absolute error, root-mean-square error, average error and standard deviation. The errors of the proposed algorithm are compared with these of the known block-matching technique with cross-correlation and interpolation in the sub-pixel space. Statistical analysis of the errors shows that the proposed algorithm provides more accurate estimates of the heart motion than the cross-correlation technique with interpolation in the sub-pixel space.     

Thresholding in correlation analyses of magnetic resonance functional neuroimaging

The definition of objective and effective thresholds in MRI of human brain function is a crucial step in the analysis of paradigm-related activations. This paper introduces a user-independent and robust procedure that calculates statistical parametric maps based on correlation coefficients. Thresholds are introduced as p values and defined with respect to the physiologic noise distribution of the individual maps. Experimental examples from the human visual and motor system rely on dynamic acquisitions of gradient-echo echo-planar images (2.0 T, TR = 2000 ms, 96 × 128 matrix) with blood oxygenation level-dependent contrast. The results demonstrate the disadvantages of thresholding with fixed correlation coefficients. In contrast, taking the individual noise into account allows for a derivation of p values and a reliable identification of highly significant activation centers. An adequate delineation of the spatial extent of activation may be achieved by adding directly neighboring pixels provided their correlation coefficients comply with a second lower p value threshold.     

基于蚁群算法的二维最大熵分割算法

由于二维最大熵分割法不仅考虑了像素的灰度信息,而且还充分利用了像素的空间邻域信息,因此能够取得较好的分割效果.但是,该方法的计算量巨大,不利于红外图像的快速处理.蚁群算法于20世纪90年代初提出,是受到蚁群集体行为的启发而提出的一种基于种群的模拟进化算法,属于随机搜索算法.该算法已经成功应用于旅行商等离散问题.将蚁群算法应用于二维最大熵法,提出了基于蚁群算法的二维最大熵分割算法.与传统的穷尽搜索法相比,求解速度提高了60倍左右.仿真实验表明,该方法快速、简单、有效.