An infrared and visible image fusion method based upon multi-scale and top-hat transforms

The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients around the zero value are very few, so we cannot sparsely represent low-frequency image information. The low-frequency component contains the main energy of the image and depicts the profile of the image. Direct fusion of the low-frequency component will not be conducive to obtain highly accurate fusion result. Therefore, this paper presents an infrared and visible image fusion method combining the multi-scale and top-hat transforms. On one hand, the new top-hat-transform can effectively extract the salient features of the low-frequency component. On the other hand, the multi-scale transform can extract highfrequency detailed information in multiple scales and from diverse directions. The combination of the two methods is conducive to the acquisition of more characteristics and more accurate fusion results. Among them, for the low-frequency component, a new type of top-hat transform is used to extract low-frequency features, and then different fusion rules are applied to fuse the low-frequency features and low-frequency background; for high-frequency components, the product of characteristics method is used to integrate the detailed information in high-frequency. Experimental results show that the proposed algorithm can obtain more detailed information and clearer infrared target fusion results than the traditional multiscale transform methods. Compared with the state-of-the-art fusion methods based on sparse representation, the proposed algorithm is simple and efficacious, and the time consumption is significantly reduced.

基于求导法的dq变换结合形态学滤波的电压暂降检测方法

电压暂降是电力系统中频繁出现的严重电能质量问题,而传统的电压暂降检测方法在实时性和精确性上仍有不足,对此提出一种基于形态学滤波器和求导法αβ-dq变换的单相电压暂降快速检测方法。详细分析了基于求导的αβ-dq变换法检测原理,通过比较不同结构元素的形态滤波器的性能,选取合适的结构元素的形态滤波器滤除系统谐波和噪声,提高算法的精确性。使用软件仿真分析,并通过使用该方法分析实验室搭建的模拟电压暂降模型采集到的数据,进一步对该方法进行验证,结果表明该方法可以快速、准确地检测出电压暂降的幅值、持续时间和相位变化。

QRS波群检测常用算法的比较

QRS波是ECG波形中最显著的部分。由于QRS波检测是所有心电信号自动分析的基础,近几十年来,各种新算法层出不穷。本文分别对差分法、匹配滤波法、带通滤波法、小波变换法、形态学方法、长度和能量变换法这六种经典QRS波检测算法以及一种基于数学形态学及小波分解相结合的算法进行了原理阐述和检测效果对比,结果表明:经典算法通常能满足需求,但当噪声干扰十分严重时,联合算法更准确。

基于形态滤波器预滤波的电压扰动检测方法

提出一种基于数学形态学滤波和求导法dq变换的电压扰动检测方法。首先为了减小待检测信号中脉冲、谐波等多种噪声对检测结果的干扰,利用自适应理论构建一种复合形态滤波器对含噪待检测信号进行预处理,然后利用多重直线形态滤波器改进的求导法dq变换对预处理后电压信号进行电压扰动检测,以快速准确地检测出待测电压的幅值、相位。搭建仿真模型,分别对自适应复合形态滤波器滤波流程、自适应复合形态滤波器性能、多重直线形态滤波器改进的求导法dq变换电压扰动检测性能进行仿真分析。最后搭建电压扰动发生硬件平台,对所提方法在扰动情况下的电压检测能力进行验证。

基于形态学的自动QT间期检测方法的研究

目的本文针对当前移动监护平台的需要,研究并设计一种新的基于多尺度形态学导数变换的心电信号特征点自动识别算法,从而实现QT间期的检测。方法引入三角形和线段作为形态学变换的结构元素对对心电信号进行多尺度形态学导数变换(multi-scale morphological derivative transform,MMDT),根据波形的形态学特征将其分为4种情况,并针对每种情况分别设计最佳方案来对Q波起点进行检测。同时引入"翼"函数选取T波的2个参照点,辅助确定T波峰位置及其终点,使得MMDT方法在检测双峰T波(90.9%)和双相T波(86.7%)时的精度和识别率得到较大改善。结果本文采用了CSE数据库中的80个心电记录对算法进行评估,数据统计结果表明该算法与小波变换方法相比其整体误差及标准差更小,对非正相T波的检测性能大幅提高。结论相比小波变换和自适应阈值方法,该算法经验参数少,运算量低,非常适合用于移动监护和HOLTER系统,具有广阔的应用前景。