光学精密工程2024，Vol.32Issue(8)：1199-1211,13.DOI:10.37188/OPE.20243208.1199
增强型金枪鱼群优化指数熵的砂粒显微图像分割
Sand microscopic image segmentation with enhanced tuna swarm optimization exponential entropy
摘要
Abstract
Microscopic image segmentation of sand grains can assist geological assessment,but it poses challenges to the accuracy of segmentation due to its variety and complex features.For such images,a seg-mentation method with enhanced tuna swarm optimization exponential entropy(ETSO-EXP)was pro-posed,which could effectively preserve the texture features of various sand grains.First of all,aiming at some deficiencies of the tuna swarm optimization(TSO)algorithm in global search and local develop-ment,a chaotic disturbance strategy,a dynamic weight strategy and a cosine disturbance strategy were proposed to enhance it.The benchmark function experiment showed that the ETSO greatly improved the convergence accuracy and slightly increased the convergence speed.Secondly,the ETSO algorithm was used to determine the segmentation threshold of the EXP,and the feasibility of the scheme was verified by taking the information content of the segmented image as the standard.Finally,a segmentation experi-ment was carried out on the Yarlung Zangbo River sand microscopic image dataset.Compared with the TSO-EXP,the image of the ETSO-EXP segmentation has a better peak signal-to-noise ratio,structural similarity,feature similarity and the optimization speed has been improved by 18.78%,6.85%,4.16%and 3.83%,respectively,and the performance is the best among the similar segmentation meth-ods.The results show that the segmentation method with the ETSO-EXP has high segmentation accura-cy and calculation speed for images with high contrast,rich texture or large differences in the size of sand debris.关键词
砂粒显微图像/图像分割/指数熵/金枪鱼群优化Key words
sand grain microscopic image/image segmentation/exponential entropy/tuna swarm opti-mization分类
计算机与自动化引用本文复制引用
王梦菲,王卫星,徐琨,李理敏..增强型金枪鱼群优化指数熵的砂粒显微图像分割[J].光学精密工程,2024,32(8):1199-1211,13.基金项目
国家自然科学重点基金项目(No.U1401252) （No.U1401252）
浙江省教育厅科研项目(No.Y202146796) （No.Y202146796）
浙江省自然科学基金资助项目(No.LTY22F020003) （No.LTY22F020003）
温州市重大科技创新项目(No.ZG2021029) （No.ZG2021029）
