Integrating patient-focused information systems in an open-architecture environment

The healthcare industry is beginning to realize that its services and information systems need to be customer-centered. The author contends that not only is the patient a primary customer of the healthcare provider, but the patient's physician, employer and insurerer are also "customers" who need patient-related information. These interrelationships demand integration.     

基于能量最小化的图像融合

图像融合是指联合两个或两个以上的图像通过某种算法得到一幅更高质量的新图像.提出了一种融合全色图像和光谱图像的方法即能量最小化方法,能量主要由两个部分组成.第1部分保证了相关细节信息的注入.第2部分保持了多光谱图像的低频信息.另外,能量还可以包含高分辨率光谱图像的先验知识和其它一些约束条件.     

基于ARM的开放式数控系统研究

在对开放式数控系统的功能要求的分析基础上,对系统的硬件和软件进行了合理的功能配置。并由此进行了基于ARM的开放式数控系统的总体设计;详细介绍了系统的原理与构成：根据系统微型化、高性能、低成本、便携性的要求,在系统硬件设计时,选用了低功耗、高性能、成本低的微处理器,选用开放源代码的UC1inUX作为实时操作系统,构成了简单易学易操作的开放式数控系统。     

Image denoising with norm weighted fusion estimators

In recent era, the weighted matrix rank minimization is used to reduce image noise, promisingly. However, low-rank weighted conditions may cause oversmoothing or oversharpening of the denoised image. This demands a clever engineering algorithm. Particularly, to remove heavy noise in image is always a challenging task, specially, when there is need to preserve the fine edge structures. To attain a reliable estimate of heavy noise image, a norm weighted fusion estimators method is proposed in wavelet domain. This holds the significant geometric structure of the given noisy image during the denoising process. Proposed method is applied on standard benchmark images, and simulation results outperform the most popular rivals of noise reduction approaches, such as BM3D, EPLL, LSSC, NCSR, SAIST, and WNNM in terms of the quality measurement metric PSNR (dB) and structural analysis SSIM indices.     

基于压缩感知理论的图像融合

像素级图像融合需要对待融合图像的像素灰度进行综合分析与处理,而巨大的数据量给图像融合带来了极大的不便。压缩感知理论的兴起提供了一种新的数据采集和压缩方法 ,它利用图像的部分信息重构原始图像,有效减轻了图像处理的计算复杂度。本文基于对压缩感知理论的研究,把压缩感知的方法应用于图像融合,并尝试通过对压缩感知观测矩阵的改进提高融合质量。文中设计出一种适应于DCT域的射线采样矩阵,通过与随机生成的观测矩阵融合效果的比较,证明该方法取得了良好的融合效果。     

Image fusion method based on nonseparable wavelets

This paper presents an image fusion method based on a new class of wavelet - nonseparable wavelet with compact support, linear phase, orthogonality, and dilation matrix . We first construct a 6 x 6 nonseparable wavelet filter bank. Using these filters the images involved are decomposed into nonseparable wavelet pyramids. Then the following fusion algorithm is proposed: for the low-frequency part, we select the average of the low-frequency subimages from both sensors. For every high-frequency subimage of each level, we select the absolute value of each pixel of the high-frequency subimage to form a new subimage, and the variance of each image patch over a 3 x 3 window in the new subimages is computed as an activity measurement. If the variance of the 3 x 3 window in one new subimage is greater than the variance of the corresponding 3 x 3 window in the other new subimage, then the center pixel value of the 3 x 3 window is selected as a new pixel value of the fused image. A new fused image is then reconstructed. The performance of the method is evaluated using entropy, root mean square error, and peak-to-peak signal-to-noise ratio. The experimental results show that this method has good vision effect. Because the nonseparable wavelet transform can extract more details from source images, all the features in the source images can be seen in the fused image, and the fused image can extract more information from the source images.Received: 5 July 2003, Accepted: 29 November 2004, Published online: 25 February 2005This research was support by NSFC under project 60085002     

基于小波邻域信息量的图像融合

针对多模图像融合问题提出了一种基于小波变换的新方法.将小波低频系数乘以加权因子1/R,减少低频部分所占整个图像的信息比例,并采取绝对值取大的融合规则选取小波低频系数;使用由方差和平均梯度构造的新的评价因子--小波邻域信息量作为融合规则选取小波高频系数.实验结果表明,该方法得到的融合图像体现出更强的融合性能.     

基于多尺度稀疏表示的图像融合方法

针对目前基于稀疏表示的常用图像融合算法计算复杂度高以及忽略图像局部特征的问题,提出多尺度稀疏表示(multi-scale sparse representation,MSR)的图像融合方法.充分利用小波多尺度分析较好突出图像局部特征的特点,将其和过完备稀疏表示有效结合;待融合图像在小波解析域中进行小波多层分解,对每个尺度的特征运用K-SVD (kernel singular value decomposition)多尺度字典进行OMP (orthogonal matching pursuit)稀疏编码,并在小波域中各个尺度中进行融合.实验结果表明,与传统的小波变换、轮廓波变换、稀疏表示融合算法相比,该算法更能保证图像局部特征的完整性,实现更好的性能.     

一种基于静态小波变换的图像融合算法

给出了一种基于静态小波多尺度边缘检测的图像融合算法,是一种适用于微光和红外图像融合的多分辨率方法。该方法选择á trous多孔算法为基础,并选取不同尺度参数的LOG算子作边缘检测,利用了图像的多尺度边缘信息确定图像的边缘位置进行融合。在小波域中,对高频信息依据边缘检测的边缘点进行融合,对低频信息利用取加权法进行融合,再进行小波逆变换重构融合图像。通过红外和微光图像的融合实验结果表明,该方法能有效地突出边缘细节,提高图像分辨效果和人眼对场景目标的发现和识别概率。     

Image fusion based on simultaneous empirical wavelet transform

Multimedia Tools and Applications - In this paper, a new multi-scale image fusion algorithm for multi-sensor images is proposed based on Empirical Wavelet Transform (EWT). Different from...