Properties of a genetic algorithm extended by a random self-learning operator and asymmetric mutations: A convergence study for a task of powder-pattern indexing

Genetic algorithms represent a powerful global-optimisation tool applicable in solving tasks of high complexity in science, technology, medicine, communication, etc. The usual genetic-algorithm calculation scheme is extended here by introduction of a quadratic self-learning operator, which performs a partial local search for randomly selected representatives of the population. This operator is aimed as a minor deterministic contribution to the (stochastic) genetic search. The population representing the trial solutions is split into two equal subpopulations allowed to exhibit different mutation rates (so called asymmetric mutation). The convergence is studied in detail exploiting a crystallographic-test example of indexing of powder diffraction data of orthorhombic lithium copper oxide, varying such parameters as mutation rates and the learning rate. It is shown through the averaged (over the subpopulation) fitness behaviour, how the genetic diversity in the population depends on the mutation rate of the given subpopulation. Conditions and algorithm parameter values favourable for convergence in the framework of proposed approach are discussed using the results for the mentioned example. Further data are studied with a somewhat modified algorithm using periodically varying mutation rates and a problem-specific operator. The chance of finding the global optimum and the convergence speed are observed to be strongly influenced by the effective mutation level and on the self-learning level. The optimal values of these two parameters are about 6 and 5%, respectively. The periodic changes of mutation rate are found to improve the explorative abilities of the algorithm. The results of the study confirm that the applied methodology leads to improvement of the classical genetic algorithm and, therefore, it is expected to be helpful in constructing of algorithms permitting to solve similar tasks of higher complexity.     

A novel algorithm of image fusion using shearlets

As a novel MGA (Multiscale Geometric Analysis) tool, shearlets are equipped with a rich mathematical structure similar to wavelets, which are associated to a multi-resolution analysis. Recently, shearlets have been used in image denoising, sparse image representation and edge detection. In this paper, as shearlet transform has the features of directionality, localization, anisotropy and multiscale, it is introduced into image fusion to obtain a fused image. The image could be decomposed by shearlet transform in any scale and any direction, and the detail information can be caught easily. Several different experiments are adopted to demonstrate that the fusion results based on shearlet transform contain more detail and smaller distortion information than any other methods does.     

Generation of mm- and Sub mm-wave Bessel Beams Using DOE’s Designed by BOR-FDTD Method and MGA

A new way for generating Bessel beams at mm and sub mm-wavelengths is presented in this paper, in which diffractive optical elements (DOE’s) are designed for converting incident Gaussian beams into Bessel beams. In order to reduce the computational burden and therefore improve the design efficiency, two measures are adopted in our design. One is a body-of-revolution finite-difference time-domain (BOR-FDTD) method that uses a two-dimensional (2-D) solution space instead of a full 3-D space and thereby saves tremendous computational resources, and that is utilized to calculate the fields diffracted by the DOE’s. The other is a microgenetic algorithm (MGA) that has been proved to be more effective than the conventional GA, and that is employed for accelerative optimization. The utility of the present design tool, which combines a MGA with a BOR-FDTD method, is demonstrated by three examples. Numerical simulation results indicate that the designed DOE’s can not only flexibly generate zero- or higher- order Bessel beams when compared with axicons, but also have higher diffraction efficiencies when compared with amplitude holograms. An erratum to this article can be found at     

结合边缘信息和图像特征信息的曲波域遥感图像融合

曲波变换是一种更适合于图像处理的多尺度几何分析方法,具有比小波变换更强的方向选择和辨识能力,而且对图像边缘的表达更优于小波.结合色度-饱合度-亮度变换将其应用于合成孔径雷达图像和多光谱图像融合可以更好地表示图像中的有用特征.首先对多光谱图像进行色度-饱合度-亮度变换,得到亮度分量Ⅰ,对雷达图像和Ⅰ分量进行曲波变换得到粗尺度系数和细节尺度系数;将雷达图像的粗尺度系数和细节尺度系数进行叠加,计算归一化的曲波系数直方图,定义边缘有效因子,利用合成孔径雷达图像的特征信息将曲波变换系数分为均匀区、非均匀区和亮点目标区.然后采用相应的融合规则对融合图像的粗尺度系数进行处理,对细节尺度系数采用简单的直接取大方法,逆变换后得到新的亮度分量.用新的亮度分量替代原亮度分量进行逆色度-饱合度-亮度变换得到最终融合结果,利用统计类指标对融合结果进行评价.实验结果表明,该方法在保持光谱信息和提高空间分辨率上都有较好的效果.     

L0-norm sparse representation based on modified genetic algorithm for face recognition

The typical sparse representation for classification (SRC) exploits the training samples to represent the test samples, and classifies the test samples based on the representation results. SRC is essentially an L₀-norm minimization problem which can theoretically yield the sparsest representation and lead to the promising classification performance. We know that it is difficult to directly resolve L₀-norm minimization problem by applying usual optimization method. To effectively address this problem, we propose the L₀-norm based SRC by exploiting a modified genetic algorithm (GA), termed GASRC, in this paper. The basic idea of GASRC is that it modifies the traditional genetic algorithm and then uses the modified GA (MGA) to select a part of the training samples to represent a test sample. Compared with the conventional SRC based on L₁-norm optimization, GASRC can achieve better classification performance. Experiments on several popular real-world databases show the good classification effectiveness of our approach.     

结合边缘信息和图像特征信息的曲波域遥感图像融合

曲波变换是一种更适合于图像处理的多尺度几何分析方法,具有比小波变换更强的方向选择和辨识能力,而且对图像边缘的表达更优于小波.结合色度-饱合度-亮度变换将其应用于合成孔径雷达图像和多光谱图像融合可以更好地表示图像中的有用特征.首先对多光谱图像进行色度-饱合度-亮度变换,得到亮度分量I,对雷达图像和I分量进行曲波变换得到粗尺度系数和细节尺度系数;将雷达图像的粗尺度系数和细节尺度系数进行叠加,计算归一化的曲波系数直方图,定义边缘有效因子,利用合成孔径雷达图像的特征信息将曲波变换系数分为均匀区、非均匀区和亮点目标区.然后采用相应的融合规则对融合图像的粗尺度系数进行处理,对细节尺度系数采用简单的直接取大方法,逆变换后得到新的亮度分量.用新的亮度分量替代原亮度分量进行逆色度-饱合度-亮度变换得到最终融合结果,利用统计类指标对融合结果进行评价.实验结果表明,该方法在保持光谱信息和提高空间分辨率上都有较好的效果.     

非下采样变换的红外与可见光图像融合

基于非下采样Contourlet变换(NSCT),提出了一种红外和可见光图像融合算法。针对低频子带系数和各带通方向子带系数分别提出了基于图像物理特征的系数加权选择方式与基于区域能量匹配的系数选择方式,即低频基于区域梯度信息、高频基于区域特征因子的加权与选择结合的图像融合算法。实验结果表明:非下采样Contourlet变换具有较快的运算速度,且经非下采样变换后能量更加集中,可提供更多的图像信息。相对于基于像素的图像融合算法,本文的图像融合算法具有更高的融合性能,是一种更适合图像融合的多尺度几何分析(MGA)工具。     

乳腺X线图像的增强与噪声抑制研究

提出了一种基于多尺度几何分析的乳腺X线图像增强与噪声抑制的新方法.首先对乳腺X线图像进行非下采样Contourlet变换分解,得到相应尺度和子带信息;然后根据变换系数的统计特性,应用广义高斯混合模型对其进行建模,并在此基础上引入贝叶斯分类方法将建模后的系数分为强弱边缘及噪声;再根据各个子带系数的类别属性,分别设计相应的非线性映射函数来对不同的系数进行自适应增强和抑制;最后采用增强和抑制后的系数对原图像进行重构,从而得到凸显病变区域特征的增强图像.实验结果表明,该方法具有良好的实用性和鲁棒性.     

基于非下采样Contourlet变换的图像融合算法

针对曲波变换图像融合的不足,提出了一种基于非下采样轮廓波变换（Non—subsampledContourletTransform,NSCT）图像融合方法。首先对已配准待融合图像进行NSCT分解;然后使用相应的融合规则对Contourlet域系数进行融合,得到融合图像的NSCT系数;最后经逆变换重构得到融合图像。通过对不同曝光度图像以及多聚焦图像进行融合实验,仿真结果表明该算法融合图像在主观视觉和客观评价指标上均取得良好效果。