电测与仪表2024，Vol.61Issue(1)：46-51,6.DOI:10.19753/j.issn1001-1390.2024.01.007
基于灰狼自适应阈值分割和改进模糊增强的红外图像NSCT增强算法
Infrared image NSCT enhancement algorithm based on gray wolf adaptive threshold segmentation and improved fuzzy enhancement
摘要
Abstract
Research on low-cost and portable infrared imaging technology is the development trend of live detection in re-cent years.In order to reduce the influence of infrared detection environment,infrared sensors and other factors,and solve the problems of infrared image noise,blur and low contrast in infrared detection,an infrared image NSCT enhance-ment algorithm based on gray wolf maximum entropy threshold segmentation and improved fuzzy enhancement is designed in this paper.The original infrared image is transformed into high frequency component and low frequency component by NSCT domain.Then,the high-frequency component with noise is de-noised by VT and enhanced by improved fuzzy en-hancement,and the low-frequency components with power equipment are segmented by gray wolf adaptive threshold,after that,they are enhanced respectively.Finally,the enhanced high-frequency components and low-frequency components are inverted NSCT to form the final enhanced image.The superiority of the algorithm in the substation power equipment infrared detection is verified through the comparison application.Compared with other algorithms,the edge strength,in-formation entropy,contrast,standard deviation and peak signal-to-noise ratio of the algorithm increases by 3.94％,2.16％,9.86％,7.45％ and 21.86％ at least.The infrared image processed by the algorithm conforms to the human visual effect,which is easier for the human eye to identify the fault,and is conducive to the detection and fault location of power equipment thermal fault.关键词
红外检测/红外图像/灰狼自适应阈值分割/改进模糊增强/NSCT变换Key words
infrared detection/infrared image/gray wolf maximum entropy threshold segmentation/improved fuzzy en-hancement/NSCT transform分类
信息技术与安全科学引用本文复制引用
许霄霄,张昕,姚强,朱佳祥,王昕..基于灰狼自适应阈值分割和改进模糊增强的红外图像NSCT增强算法[J].电测与仪表,2024,61(1):46-51,6.基金项目
国家自然科学基金资助项目(61673268) （61673268）
