一种医学X射线图像动态范围扩展方法

针对使用影像增强器或电荷耦合器(CCD)的数字X射线成像设备(DR)动态范围小的不足,提出一种采用尺度空间分解的X射线图像动态范围扩展方法.首先,对成像对象进行2次不同球管电压下的低剂量曝光,得到2幅小动态范围图像,然后在尺度空间对2幅图像进行分解,最后通过重构2幅图像的分解分量重建1幅宽动态范围图像.实验结果表明,该方法能使图像包含的成像对象感兴趣区域由2个扩展到3个甚至更多.2次曝光的皮肤表面吸收剂量总和为2.646×104Gy/cm2,却并未增加患者皮肤表面吸收剂量.将此方法用于使用影像增强器或电荷耦合器的数字X射线成像设备,有利于提高这些设备的利用率.

基于二阶矩显著性估算的局部不变特征提取

借鉴图像显著性区域的检测思想,提出一种基于局部二阶矩显著性估算的局部不变特征提取算法。利用二阶矩矩阵对尺度空间下局部图像的各向异性程度的估算作用,在图像尺度空间中对局部特征提取区域的信息显著性进行评估,并根据显著性进行局部不变特征的提取,提取出拥有较高显著性的局部不变特征,增加了匹配特征点对的数量和尺度跨度。真实图像实验证明,该算法在保持局部特征各种不变性的基础上有效地提高了特征提取和匹配算法的性能。

基于深度信息的人体检测窗口快速提取方法

为改善图像尺度空间搜索方法在人体检测窗口提取中的检测窗口数量多、检测消耗时间长的问题,在深入分析Kinect深度数据特点的基础上,给出了一种基于深度信息快速提取人体检测窗口的方法.该方法通过计算待检测深度图像中深度值出现频次的极大值,根据出现频次极大值对应深度值区域的几何中心确定检测窗口候选位置的中心,并进一步基于深度值和人体高度间的关系,确定检测窗口的尺寸,从而实现快速提取一系列人体检测窗口.最后,从检测窗口数量、响应时间和准确度等3个方面对该提取方法进行了评估,结果表明:该方法的总体效果良好,缩短了检测时间,并且准确率较高,达到了提高人体检测效率的目的.

Multi-Embed Nonlinear Scale-Space for Image Trust Root Generation

An image trust root is a special type of soft trust root for trusted computing. However,image trust root generation is difficult,as it needs a corresponding stable logic feature generation model and algorithm for dynamical and sustained authentication. This paper proposes a basic function of constructing new scale-spaces with deep detecting ability and high stability for image features aimed at image root generation. According to the heat distribution and spreading principle of various kinds of infinitesimal heat sources in the space medium,a multi-embed nonlinear diffusion equation that corresponds to the multi-embed nonlinear scale-space is proposed,a HARRIS-HESSIAN scale-space evaluation operator that aims at the structure acceleration characteristics of a local region and can make use of image pixels' relative spreading movement principle was constructed,then a single-parameter global symmetric proportion(SPGSP) operator was also constructed. An authentication test with 3000 to 5000 cloud entities shows the new scale-space can work well and is stable,when the whole cloud has 5%-50% behavior with un-trusted entities. Consequently,it can be used as the corresponding stable logic feature generation model and algorithm for all kinds of images,and logic relationships among image features for trust roots.

A Novel Approach for Unsupervised Segmentation of Homogeneous Regions in Gray-scale Images

An improved approach for JSEG is presented for unsupervised segmentation of homogeneous regions in gray-scale images. Instead of intensity quantization, an automatic classification method based on scale space-based clustering is used for nonparametric clustering of image data set. Then EM algorithm with classification achieved by space-based classification scheme as initial data used to achieve Gaussian mixture modelling of image data set that is utilized for the calculation of soft J value. Original region growing algorithm is then used to segment the image based on the multiscale soft J-images. Experiments show that the new method can overcome the limitations of JSEG successfully.

Deeplearning method for single image dehazing based on HSI colour space

The traditional single image dehazing algorithm is susceptible to the prior knowledge of hazy image and colour distortion.A new method of deep learning multi-scale convolution neural network based on HSI colour space for single image dehazing is proposed in this paper,which directly learns the mapping relationship between hazy image and corresponding clear image in colour,saturation and brightness by the designed structure of deep learning network to achieve haze removal.Firstly,the hazy image is transformed from RGB colour space to HSI colour space.Secondly,an end-to-end multi-scale full convolution neural network model is designed.The multi-scale extraction is realized by three different dehazing sub-networks:hue H,saturation S and intensity I,and the mapping relationship between hazy image and clear image is obtained by deep learning.Finally,the model was trained and tested with hazy data set.The experimental results show that this method can achieve good dehazing effect for both synthetic hazy images and real hazy images,and is superior to other contrast algorithms in subjective and objective evaluations.

The spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing

The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.