Multiscale toggle contrast operator-based mineral image enhancement

Mineral image is one type of crucial data for mineral research and analysis. However, some mineral images are not clear in some cases. To efficiently enhance mineral images and therefore benefit the applications of these images, a multiscale toggle contrast operator based algorithm is proposed in this paper. First, the toggle contrast operator is discussed. Secondly, the multiscale toggle contrast operator using structuring elements with different sizes is given. Thirdly, the multiscale toggle contrast operator is used to enhance the original image at different scales. Finally, the final enhanced image is constructed from the multiscale enhanced results. Because multiscale structuring elements are used, the algorithm performs efficiently and produces few noises. Experimental results show that the proposed algorithm is efficient for mineral image enhancement. More importantly, the proposed algorithm could be also used in other types of images, such as visual image, medical image and so on, for image enhancement.     

Enhancing microscopy images of minerals through morphological center operator‐based feature extraction

To well enhance the mineral image and image details obtained by microscopes, an effective mineral image enhancement algorithm through feature extraction using the morphological center operator is proposed in this work. First, mineral image feature extraction based on the morphological center operator is proposed and discussed. Second, the multiscale extension of the mineral image feature extraction is given by using the multiscale structuring elements. Third, the important mineral image features at multiscales of image are extracted and used to construct the final mineral features for mineral image enhancement. Finally, the original mineral image is well enhanced through importing the extracted final mineral image features into the original mineral image. Experimental results on different types of microscopy images of minerals verified the effective performance of the proposed algorithm for microscopy mineral image enhancement. Microsc. Res. Tech., 2013. © 2012 Wiley Periodicals, Inc.     

Measure of microscopy mineral image clarity constructed using the sequentially combined toggle and top‐hat based contrast operator

Correctly and effectively quantifying the clarity of microscopy mineral image is important and useful to produce clear image for mineral analysis. To well quantify the clarity of mineral image, an effective measure constructed using the sequentially combined toggle and top‐hat based contrast operator is proposed in this article. First, the sequentially combined toggle and top‐hat based contrast operator is used to extract the effective features which represent the clarity of mineral image. Second, the multiscale clarity features corresponding to each scale are extracted. Finally, after combining the multiscale clarity features in one feature image, the mean value of the feature image is constructed as the measure of mineral image clarity. Because the sequentially combined toggle and top‐hat based contrast operator could effectively extract the clarity features, the clarity features are directly used as the measure of image clarity. Therefore, the proposed measure is effective and reasonable. The experimental results on different types of microscopy mineral images verified the effective performance of the proposed measure. Microsc. Res. Tech. 76:811–817, 2013. © 2013 Wiley Periodicals, Inc.     

自适应差异多尺度形态学的风力机叶片红外图像增强研究

为提高采用红外检测技术检测风力发电机叶片内部缺陷的能力,改善红外热像图的清晰度低、背景光照不均和细节识别能力弱等问题,提出了一种自适应差异多尺度形态学(ADMM)的图像增强算法.在膨胀和腐蚀运算中分别选择不同尺度的结构元素作为操作对象,采用对比度改善系数比最大值作为多尺度及差异尺度选择的目标函数;利用开、闭及顶帽运算的...     

Alignment with sub‐pixel accuracy for images of multi‐modality microscopes using automatic calibration

A biological specimen is often imaged with various imaging modalities, and it is crucial that such images are well aligned to best reveal physiological structures and functions of the specimen for in‐depth analyses. In this paper, we present a methodology for automatic calibration of multiple optical imaging modalities within the x–y detector plane using a custom chrome‐on‐glass target and an automatic and accurate registration algorithm. The target contains lines crossing at random angles, and our method of registration is based on the alignment of salient features extracted from the lines within the individual images. Once spatial relationships are found between the various detectors and applied to the resultant images, no further registration is required for all static samples, and the registered images serve as the starting point for registration of dynamic samples, where the remaining misalignment is caused by sample movement. We have validated our algorithm with 40 inter‐modal and 30 intra‐modal image pairs, and the success rates are 95 and 100%, respectively, with sub‐pixel accuracy. This methodology is widely applicable to any multi‐modal microscope that combines a number of imaging modalities on a common platform assuming images of the target can be obtained.     

Breast histopathology image segmentation using spatio‐colour‐texture based graph partition method

This paper proposes a novel integrated spatio‐colour‐texture based graph partitioning method for segmentation of nuclear arrangement in tubules with a lumen or in solid islands without a lumen from digitized Hematoxylin–Eosin stained breast histology images, in order to automate the process of histology breast image analysis to assist the pathologists. We propose a new similarity based super pixel generation method and integrate it with texton representation to form spatio‐colour‐texture map of Breast Histology Image. Then a new weighted distance based similarity measure is used for generation of graph and final segmentation using normalized cuts method is obtained. The extensive experiments carried shows that the proposed algorithm can segment nuclear arrangement in normal as well as malignant duct in breast histology tissue image. For evaluation of the proposed method the ground‐truth image database of 100 malignant and nonmalignant breast histology images is created with the help of two expert pathologists and the quantitative evaluation of proposed breast histology image segmentation has been performed. It shows that the proposed method outperforms over other methods.     

低比度钢板微小缺陷的图像增强和分割

针对低对比度、微小的钢板表面缺陷,提出了基于图像增强的图像分割算法来有效分割和识别缺陷目标。采用小波变换与同态滤波结合算法对图像进行增强处理,不仅消除照度不均的影响,还突出了缺陷细节的信息,达到图像增强的效果。最后,利用粒子群算法优化最大类间方差法参数（PSO-Otsu）确定增强后图像的最佳阈值,并结合Canny算子进行缺陷检测。对比其他算法,该算法在检测低对比度的微小缺陷上取得了良好的效果。     

Nonparametric de‐noising filter optimization using structure‐based microscopic image classification

The Local Polynomial Approximation (LPA) is a nonparametric filter that performs pixel‐wise polynomial fit on a certain neighborhood. This filter can be supported by the Intersection of Confidence Interval rule (ICI) as an adaptation algorithm to identify the most suited neighborhood at which the polynomial assumptions provide superior fit for the observations. However, the LPA‐ICI is considered to be a near‐optimal de‐noising filter. Moreover, the ICI rule has several parameters that affect its performance. The current study applied an optimization algorithm, namely the Particle swarm optimization (PSO) to determine the optimal ICI parameter values for microscopic images de‐noising. As the ICI parameters are image's structure based, bag‐of‐features classifier is used to classify the images based on their structure into different classes. Afterward, a generated optimal ICI parameters' table was created using the LPA‐ICI‐PSO for further direct use without optimization. This table included the optimal ICI parameters based on the image structure. Based on the image category, the generated table can be used to attain the suitable ICI optimal parameters without using PSO. This guarantees less computational time along with the optimal de‐noising compared to the LPA‐ICI as established by the performance metrics. The experimental results established the superiority of the proposed LPA‐ICI‐PSO over the classical LPA‐ICI filter.     

面向机柜表面缺陷检测的不均匀光照和低亮度图像增强方法

光照条件是大尺寸机柜表面缺陷检测的重要影响因素。当光照分布不均匀或光照强度不足时,采集得到的机柜表面图像质量低,造成缺陷检测误差。为此,提出一种融合卡通纹理分解和最优双曲正切曲线的图像增强方法。首先,采用导向滤波将机柜表面图像分解为卡通图和纹理图,利用高斯尺度空间理论建立光照模型,实现不均匀光照去除;其次,研究图像的双曲正切曲线性质,通过图像加权拉伸实现低亮度图像增强;最后,采用对比度、亮度和灰度方差乘积对图像增强效果进行评价,同时对增强前和增强后的图像进行缺陷检测,进行对比分析验证。实验结果表明,该方法能实现光照不均且低亮度的机柜表面图像增强,机柜表面缺陷检测的准确率显著提升,召回率提高了29%,F值提高了21%。     

Three‐dimensional imaging of whole mouse models: comparing nondestructive X‐ray phase‐contrast micro‐CT with cryotome‐based planar epi‐illumination imaging

In this study, we compare two evolving techniques for obtaining high‐resolution 3D anatomical data of a mouse specimen. On the one hand, we investigate cryotome‐based planar epi‐illumination imaging (cryo‐imaging). On the other hand, we examine X‐ray phase‐contrast micro‐computed tomography (micro‐CT) using synchrotron radiation. Cryo‐imaging is a technique in which an electron multiplying charge coupled camera takes images of a cryo‐frozen specimen during the sectioning process. Subsequent image alignment and virtual stacking result in volumetric data. X‐ray phase‐contrast imaging is based on the minute refraction of X‐rays inside the specimen and features higher soft‐tissue contrast than conventional, attenuation‐based micro‐CT. To explore the potential of both techniques for studying whole mouse disease models, one mouse specimen was imaged using both techniques. Obtained data are compared visually and quantitatively, specifically with regard to the visibility of fine anatomical details. Internal structure of the mouse specimen is visible in great detail with both techniques and the study shows in particular that soft‐tissue contrast is strongly enhanced in the X‐ray phase images compared to the attenuation‐based images. This identifies phase‐contrast micro‐CT as a powerful tool for the study of small animal disease models.     

基于DRGAN和支持向量机的合成孔径雷达图像目标识别

为解决SAR图像目标识别中样本缺乏和方位角敏感问题,提出了一种基于DRGAN和SVM的SAR图像目标识别算法。首先,采用多尺度分形特征对SAR图像进行增强,经过分割得到目标二值图像,基于Hu二阶矩估计目标的方位角。然后对估计得到的目标方位角进行量化编码,结合原始图像作为输入,对设计的DRGAN模型参数进行训练与优化。由于DRGAN中的深度生成模型将目标姿态与外观表示进行解耦设计,故可利用该模型将SAR图像样本变换到同一方位角区间。基于变换后的训练样本分别提取归一化灰度特征,利用SVM训练分类器。采用MSTAR数据集在多个不同操作条件下对提出的算法进行测试,实验结果表明,在带变体的标准操作条件下,能够达到97.97%的分类精度,优于部分基于CNN模型的分类精度,在4种扩展操作条件下的分类精度分别为97.83%,91.77%,97.11%和97.04%,均优于传统方法的分类精度。在SAR图像目标方位角估计存在一定误差的情况下,训练得到的GAN模型作为SAR图像目标旋转估计器,能够使得在不进行复杂样本预处理的前提下,仍然取得较高的SAR图像目标识别精度。     

多图像融合Retinex用于弱光图像增强

为解决弱光图像增强过程中对比度增强和自然度保持问题,本文提出一种基于Retinex的多图像自适应加权最小二乘滤波算法。首先,在图像的每个像素的R,G,B三通道中找到最大亮度值作为该像素的初始照明估计,根据Retinex理论生成反射图像,并通过形态学闭合方式调整反射图;接着,在初始照明图基础上,通过Gamma变换和双对数变换方法分别生成全局对比度增强图和局部自然度保持照明图;随后,设计一种自适应加权最小二乘滤波融合策略将三幅照明图融合成最终照明估计图;最后合成上述的最终照明图和调整反射图以获得弱光增强后的图像。实验结果表明,本文所提出算法的亮度顺序差(LOE)及盲图像质量评价(NIQE)值更低,可同时降低到4.12和3.25,较其他方法表现出更好的增强效果。证明了本文算法能有效地增强弱光图像对比度,同时保持图像自然度。     

Signal‐to‐noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky–Golay filters and weighted least squares error

A new technique based on cubic spline interpolation with Savitzky–Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real‐time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky–Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal‐to‐noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation‐based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real‐time SEM images, without generating corruption or increasing scanning time.     

细节保留的多曝光图像融合

针对传统的多曝光图像融合算法存在的细节丟失严重和鬼影现象,提出了一种细节保留的多曝光图像融合算法。该算法首先计算曝光序列的3个特征指标:图像细节、曝光亮度和色彩信息,其中图像细节通过引导滤波计算,曝光亮度的权值由高斯方程分配,而曝光序列的色彩信息用色彩饱和度表示。然后,利用差分图和邻域相关系数检测多曝光序列中运动物体,利用3个特征指标和运动目标检测结果分别计算静态场景和动态场景的融合权值图。为了消除噪声的影响,采用递归滤波器来修正融合权值图。最后,采取加权融合的方式得到融合图像。选取10组不同的曝光序列,分别从主观和客观两方面与6种传统的融合算法进行了比较。实验结果表明,本文算法保留了丰富的细节信息,呈现出了更加生动自然的现实场景,并且有效去除了由运动物体产生的鬼影现象,效果优于其他比较算法,在静态场景和动态场景的曝光融合中都取得了好的效果。     

应用参数化对数模型增强图像细节及对比度

传统红外图像增强方法由于未考虑人眼视觉特性而存在"超区间值",故极易丢失图像细节。本文结合对数图像处理（LIP）理论,提出了基于参数化对数模型（PLIP）的平台直方图均衡图像增强方法。该方法首先将图像变换为灰度色调函数;然后应用PLIP模型对变换后的图像进行重建,结合平台直方图均衡化来增强图像的对比度和细节信息,并利用图像评价函数（EMEE）与信息熵（En）确定模型的参数值。最后,研制了算法硬件平台,分别用中波和长波红外成像系统对算法进行了实验验证。实验结果显示：该方法对不同场景的图像均可取得很好的增强效果,图像的EMEE值比传统的平台直方图均衡算法提高至少5倍以上,对改善图像质量和视觉效果具有实用价值。     

二阶Wedgelet快速分解及其在工业CT图像线特征提取中的应用

为了对工业CT(ICT)图像中的线特征进行有效提取,提出一种基于二阶Wedgelet快速分解的特征提取方法。首先,介绍了二阶Wedgelet分解的定义,根据二阶Wedgelet基函数的构成将其划分为3种(同边型、邻边型、对边型)不同类型。根据不同类型基函数间的空间关系设计出二阶Wedgelet快速分解算法。在此基础上,在不同的单尺度上分别提取了各自的候选线特征。最后,结合多尺度四叉树结构,融合不同单尺度的候选线特征得到最终提取结果。对实际ICT图像进行了线特征提取实验,对比试验结果验证了提出方法的有效性和先进性,表明基于二阶Wedgelet快速分解的ICT图像线特征提取方法能有效地提取ICT图像感兴趣的线特征,对其它类型图像的线特征提取也具有一定的参考价值。     

基于小波变换纹理一致性测度的遥感图像融合算法

本文提出了一种基于小波变换的遥感图像融合算法，利用多分辨小波变换的系数，采用低频图像的小波系数最小值作为融合后的低频系数，高频图像根据纹理一致性测度的纹理检测确定融合规则，调整高频小波系数大小。利用小波变换对图像相对应的低频分量及各方向细节分量进行针对性融合处理，很好地将来自不同图像的特征与细节融合在一起，并对融合图像质量进行了对比评价。实验结果表明，这种方法能够在保留图像微小细节方面获得满意的结果，这种算法有效且优于传统的图像融合方法。     

Color‐based tumor tissue segmentation for the automated estimation of oral cancer parameters

This article presents an automatic color‐based feature extraction system for parameter estimation of oral cancer from optical microscopic images. The system first reduces image‐to‐image variations by means of color normalization. We then construct a database which consists of typical cancer images. The color parameters extracted from this database are then used in automated online sampling from oral cancer images. Principal component analysis is subsequently used to divide the color features into four tissue types. Each pixel in the cancer image is then classified into the corresponding tissue types based on the Mahalanobis distance. The aforementioned procedures are all fully automated; in particular, the automated sampling step greatly reduces the need for intensive labor in manual sampling and training. Experiments reveal high levels of consistency among the results achieved using the manual, semiautomatic, and fully automatic methods. Parameter comparisons between the four cancer stages are conducted, and only the mean parameters between early and late cancer stages are statistically different. In summary, the proposed system provides a useful and convenient tool for automatic segmentation and evaluation for stained biopsy samples of oral cancer. This tool can also be modified and applied to other tissue images with similar staining conditions. Microsc. Res. Tech. 2009. © 2009 Wiley‐Liss, Inc.     

采用PHOG融合特征和多类别Adaboost分类器的行为识别

为了解决类能量图易受人体运动时间和位置移动等因素影响而难以有效描述动作细节特征的问题,本文提出了一种基于类能量图金字塔梯度直方图(PHOG)融合特征和多类别Adaboost分类器的人体行为识别方法。该方法首先对经过躯体配准的运动人体目标轮廓图像构造平均运动能量图(AMEI)和增强的运动能量图(EMEI),分别提取其分层梯度方向直方图(PHOG)特征并进行串联融合,作为一种多层次的行为特征描述;然后使用基于查找表的LUT-Real Adaboost算法设计多类别分类器,实现图像中人体行为动作的识别。实验结果显示其在典型的人体动作数据集DHA上的正确识别率达97.6%,高于其它采用单一特征描述和SVM等分类器的方法。表明该方法将整体与局部特征相结合,可以有效描述不同尺度下的动作细节特征,增强了人体行为特征的描述能力,提高了识别性能。     

基于多尺度分析的人脸识别算法研究

针对传统的人脸识别算法易受光照等因素影响的缺点,提出了一种基于多尺度分析的人脸识别算法。首先,对采集到的人脸图像进行预处理,去除噪声,减弱无用信息,增强有用信息;然后,在粗尺度上采用形态学梯度边缘检测算法对人脸轮廓进行提取,缩小人脸库的搜索范围;最后,在细尺度上对人脸的不变特征进行提取,采用Harris角点检测算法对在粗尺度上得到的边缘图像进行特征提取,减少计算量,提高了识别速度。在所拍摄的人脸库上对算法进行验证,实验表明,对易于进行轮廓提取的人脸图像的识别速度较快,精度较高。