Fractal properties of AlGeNi layers on GaAs surfaces

The thermal interactions of thin AlGe and AlNiGe layers with a bulk GaAs monocrystal were investigated. The heat treatment of these systems was carried out in the working chamber of a scanning electron microscope. The SEM pictures were analysed using a fractal mathematical technique. It was found that the surface of the samples has fractal character. No temperature dependence of the fractal dimension was observed. The samples were also studied using the structural entropy versus filling factor maps of the samples in order to find their localization properties. The SEM pictures of AlGe perform mostly as a Gaussian functions, whereas the AlNiGe samples show usually a behaviour with exponential decay.     

Fractal properties of gold, palladium and gold-palladium thin films on InP

Thermal interaction of indium phosphide (InP) bulk compound semiconductor with thin gold metal films was investigated in the course of the present work. The interaction of the InP/Au system resulted in a pattern showing fractal dimensions. The temperature dependence of the fractal parameters was investigated in a broad temperature range from 200 to 600 °C. No significant temperature dependence of the fractal dimension was observed.The same calculations will be presented for Au/InP and AuPd/InP systems. Our calculations show that the Pd-based contacts have a different behaviour than AuGe metallization where a strong temperature dependence of the fractal number was observed earlier.Another topology measure, the structural entropy is also calculated for the samples. The structural entropy is usually applied for determining the type of the localization of charge distributions, but it can also be used for generalized charges, such as the lightness of the pixels of an electron microscopy picture.     

Content-based image retrieval using color vector angle difference histogram

Abstract

This paper presents an innovative method to represent an image for image retrieval in Content-Based Image Retrieval (CBIR), using a color vector angle differences histogram approach (CVADH), which is different from the existing histograms, such as color histograms, gray level co-occurrences (GLCM), color distribution entropy (CDE), or just counting the frequency number of pixels or neighbors. In contrast to other methods, the CVADH takes account of the color difference and spatial information of two pixels in RGB color space. Furthermore, it directly leverages color clues to extract shape features to show differences given different backgrounds. Experimental results show that the scheme is more efficient than traditional ones that have been developed for CBIR.     

Impact of space charges on the saturation curves of ionization chambers

We apply a perturbation method to quantify the impact of space charge accumulation on the output current of ionization chambers. We then obtain the formulae of the saturation curves delivered by these detectors under the form of series for which we calculate the first coefficients. The spatial variations of the primary charge density are taken into account in the calculations. For uniform ionization rates, we demonstrate that the amplitude of the space charge perturbations depends only on the values of the electronic and ionic Langevin factors of the filling fluid. We show that the influence of space charges can be neglected when the Langevin factor, λ, is small. These results were confirmed using numerical resolutions of the charge propagation equations. Finally, we evaluate the voltage that delimits the transition between the recombination regime and the saturation plateau when the space charges are taken into account.     

Structural sparseness and spatial phase alignment in natural scenes

The Fourier phase spectrum plays a central role regarding where in an image contours occur, thereby defining the spatial relationship between those structures in the overall scene. Only a handful of studies have demonstrated psychophysically the relevance of the Fourier phase spectrum with respect to human visual processing, and none have demonstrated the relative amount of local cross-scale spatial phase alignment needed to perceptually extract meaningful structure from an image. We investigated the relative amount of spatial phase alignment needed for humans to perceptually match natural scene image structures at three different spatial frequencies [3, 6, and 12 cycles per degree (cpd)] as a function of the number of structures within the image (i.e., "structural sparseness"). The results showed that (1) the amount of spatial phase alignment needed to match structures depends on structural sparseness, with a bias for matching structures at 6 cpd and (2) the ability to match partially phase-randomized images at a given spatial frequency is independent of structural sparseness at other spatial frequencies. The findings of the current study are discussed in terms of a network of feature integrators in the human visual system.     

工件表面低对比度缺陷快速准确识别方法

针对工件表面低对比度缺陷,提出一种基于图像网格的灰度统计分析识别新方法,首先构造利用空域和值域信息的滤波方式,对原始图像进行处理;然后对图像进行网格划分,并对网格图像的灰度值熵统计分析,实现对缺陷目标的识别。实验结果表明:对低对比度缺陷识别准确率达到97.1%。     

基于匹配调节法则和梯度约束模型的图像修复算法

目的针对当前较多图像修复算法难以根据不同纹理结构来自适应调整修复块的尺寸,导致修复结果中存在不连续效应和模糊效应等不足,提出一种结合匹配调节法则和梯度约束模型的图像修复算法。方法首先,利用平滑因子对置信度项进行约束,构造优先级判定模型,对待修复块的优先级进行度量,确定优先修复块。随后,通过SSD模型度量样本块之间的匹配结果,并根据匹配结果制定匹配调节法则,使得样本块能根据匹配度自适应调节其大小,以提高修复质量。最后,将梯度模值块中像素点的均方距离度量结果与样本块中像素点相结合,构造梯度约束模型,用以获取最佳匹配块对待修复块进行填充修复。结果实验结果表明,与当前图像修复算法相比,该算法修复的图像具有更好的修复质量,在像素丢失率较高的情况下,仍然具有较高的相似度值。结论所提算法具备较好的修复视觉质量,可用于被大面积损坏图像的修复。     

数字图像单个像元分形维数的特征与计算方法

通过分形理论把空间结构信息引入遥感分类中,必须解决分形维数计算的问题,为此提出了一种通过相邻像元间灰度值大小的变化计算数字图像单个像元分形维数的算法。选定计算窗口值 L 后,运用所编程序对 TM 遥感数据进行运算,并得到所需分维值数据。发现所得分维值随窗口值 L 增大而减小;大窗口值的分维值图像较抽象,但建议窗口值不超过 61;小窗口值的分维值图像较清晰,但窗口值不能小于 5。     

基于条件生成式对抗网络的AFM图像盲重构方法

针对原子力显微镜（AFM）成像过程中针尖展宽效应引起的误差,提出一种基于条件生成式对抗网络（CGAN）的AFM图像盲重构方法。首先,以pix2pixHD模型为基础,通过全局生成网络对仿真样本数据进行对抗训练,引入AFM测量数据采用局部提升网络联合训练;最后,特征匹配损失函数以用于提升栅格边缘横向分辨力。实验结果表明:对于线宽8μm一维矩形栅格在AFM下的测量图像进行盲重构,重构图像标准差为0.33μm×0.45μm,具有较高的成像分辨力,有利于提升AFM图像一维栅格测量的准确度。     

斜拉网格结构的结构形式、应用及特性在国内的研究进展

斜拉网格结构是目前应用较为广泛的刚柔混合结构体系之一,由塔柱、拉索、网格结构组合而成。系统的对该类结构体系在我国的发展进行了回顾及展望。首先,对该混合结构的结构形式发展及部分工程应用进行了介绍;之后,分别回顾了斜拉平板网架结构和斜拉空间网壳结构的结构特性研究,从考虑结构几何非线性,参数振动和模态跃迁等因素,突出分析了斜拉空间网壳结构复杂的动力特性行为;最后,分析尚存的问题,对今后的发展进行了展望。     

Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

An image compression method based on multiple models for the probabilities of patterns

This article proposes an image compression method based on multiple models for the probabilities of patterns (MMPP method) to encode a gray‐level image f. First, the MMPP method employs a median edge detector (MED) to reduce the entropy of f. The intensities of two adjacent pixels in an image are usually close to each other. A base switching transformation (BST) is then used to lessen the spatial redundancy of f. Finally, the arithmetic encoding method is applied to further encode the data generated after the processing of MED and BST. To reduce the memory space required to hold f, the MMPP method classifies the data and then compresses the data in each cluster by the arithmetic encoding method based on different probability tables. The experimental results show that mostly the MMPP method can provide better efficiency in memory space than the lossless JPEG 2000 method does. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 362–368, 2009     

Resampling Factor Estimation via Dual-Stream Convolutional Neural Network

The estimation of image resampling factors is an important problem in image forensics. Among all the resampling factor estimation methods, spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest. However, because of inherent ambiguity, spectrum-based methods fail to discriminate upscale and downscale operations without any prior information. In general, the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image. Firstly, the resampling process will introduce correlations between neighboring pixels. In this case, a set of periodic pixels that are correlated to their neighbors can be found in a resampled image. Secondly, the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear peaks. Hence, in this paper, we propose a dual-stream convolutional neural network for image resampling factors estimation. One of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled images. The other is frequency stream that discovers the differences of spectrum between rescaled and original images. The features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain, which is later fed into softmax layer for resampling factor estimation. Experimental results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.     

Lattice Distortion Analysis Directly from High Resolution Transmission Electron Microscopy Images—the LADIA Program Package

Direct strain mapping from high resolution transmission electron microscopy images is possible for coherent structures.At proper imaging conditions the intensity peaks in the image have a constant spatial relationship with the projected atom columns.This allows the determination of the geometry of the projected unit cell without comparison with image simulations.The fast procedure is particularly suited for the analysis of large areas.The software package LADIA is written in the PV-WAVE code and provides all necessary tools for image processing and analysis.Image iintensity peaks are determined by a cross-correlation technique,which avoids problems from noise in the low spatial frequency renage.The lower limit of strain that can be detected at a sampling rate of 44 pixels/nm is ≈2%.     

Maximum entropy methods in coherent radar imaging

There are some compelling reasons for viewing the problem of image reconstruction from noisy or incomplete data as one of statistical estimation, i.e., of choosing, from the infinity of images consistent with the data, that image which, in some statistical sense, is most plausible. Among these reasons are the soundness of the philosophical underpinning of the resulting image reconstruction process, a greater realization of the image resolution which is inherent in the data, and freedom from many of the artifacts encountered in commonly used ad hoc reconstruction schemes. One successful technique employing a principle of statistical inference is the maximum entropy technique, in which the data-consistent image with maximum configurational entropy is chosen. It is a computationally intensive approach involving a conjugate gradient search over a convex function of a vector in a space of dimensionality equal to the number of image pixels. This technique has been employed with success in situations where the data samples are modeled as linearly related to a real non-negative object. We investigate application of maximum entropy image reconstruction to the problem of high-resolution radar diagnostic imaging. The problem differs from others in which maximum entropy has been applied in that the object to be imaged is complex. Although the desired image is of the magnitude of the complex object and is thus real and non-negative, there is no linear relationship beween object magnitude and data. Rather, the data are linearly related to the complex object. Several earlier proposed methods for applying the maximum entropy principle to this problem are identified and analyzed. A method that more closely approximates true Bayesian estimation is proposed.     

Image processing based on seeded spontaneous optical pattern formation by optoelectronic feedback

Spontaneous optical pattern formation from an initial seed optical pattern in an optoelectronic system with optical diffractive feedback is investigated experimentally. We demonstrate that the temporal evolution of the spontaneously formed patterns exhibits a contrast enhancement effect, a spatial filtering effect, and filling of vacant space while the surrounding structures are maintained. These effects allow us to perform image processing of natural fringe patterns, i.e., in our experiments, fingerprint patterns. We also demonstrate image processing with defect invariance for fingerprint patterns.     

Optical filling algorithm

The technique of optical symbolic substitution is applied to development of a new filling algorithm that reconstructs an image from its boundary in constant time, whereas known filling algorithms have at least O(n log N) time complexity, where n is the number of boundary pixels of a polygon and N is the diameter of the image expressed in terms of pixels.     

斜拉空间网格结构的非线性静力分析

斜拉空间网格结构是塔柱、斜拉索和空间网格协同工作的杂交结构,刚柔相济,受力合理。考虑了不同施工阶段塔柱的弹性支承作用和斜拉索的几何非线性影响,局部修正了网格结构的刚度方程,并将斜拉索施加一定的预拉力,以达到增强结构刚度、减少用钢量的目的。算例分析表明,大跨度屋盖采用斜拉空间网格结构能明显降低杆件内力,减小结构挠度,改善整个结构的受力性能。     

Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed enhancement model exploits the pixel’s probability representations for image enhancement. Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels, yielding an overall better details of the kidney MRI scans. In the second stage, the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans. The experimental results showed an average of 95.60% dice similarity index coefficient, which indicates best overlap between the segmented bodies with the ground truth. It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.     

An adaptive image denoising method based on local parameters optimization

In image denoising algorithms, the noise is handled by either modifying term-by-term, i.e., individual pixels or block-by-block, i.e., group of pixels, using suitable shrinkage factor and threshold function. The shrinkage factor is generally a function of threshold and some other characteristics of the neighbouring pixels of the pixel to be thresholded (denoised). The threshold is determined in terms of the noise variance present in the image and its size. The VisuShrink, SureShrink, and NeighShrink methods are important denoising methods that provide good results. The first two, i.e., VisuShrink and SureShrink methods follow term-by-term approach, i.e., modify the individual pixel and the third one, i.e., NeighShrink and its variants: ModiNeighShrink, IIDMWD, and IAWDMBMC, follow block-by-block approach, i.e., modify the pixels in groups, in order to remove the noise. The VisuShrink, SureShrink, and NeighShrink methods however do not give very good visual quality because they remove too many coefficients due to their high threshold values. In this paper, we propose an image denoising method that uses the local parameters of the neighbouring coefficients of the pixel to be denoised in the noisy image. In our method, we propose two new shrinkage factors and the threshold at each decomposition level, which lead to better visual quality. We also establish the relationship between both the shrinkage factors. We compare the performance of our method with that of the VisuShrink and NeighShrink including various variants. Simulation results show that our proposed method has high peak signal-to-noise ratio and good visual quality of the image as compared to the traditional methods: Weiner filter, VisuShrink, SureShrink, NeighBlock, NeighShrink, ModiNeighShrink, LAWML, IIDMWT, and IAWDMBNC methods.