摘要
针对传统红外与可见光图像融合结果存在目标模糊、信息丢失问题,提出一种基于目标增强与鼠群优化的红外与可见光图像融合方法,记为TERSFuse。为了减少融合结果中原始图像细节信息丢失,分别构建了红外对比度增强模块和基于亮度感知的可见光图像增强模块;利用拉普拉斯金字塔变换对红外和可见光增强图像进行多尺度分解,从而得到对应的高、低频图像;为了使融合结果充分保留原始图像信息,分别采用“最大绝对值”规则对红外和可见光高频图像进行融合以及通过计算权重系数对低频图像进行融合;设计了基于鼠群优化的图像重构模块以实现高频图像和低频图像重构权重的自适应分配,进而提高融合图像的视觉效果。为了验证所提算法优势,与7种经典融合算法进行比较,实验结果表明所提算法不仅具有良好的视觉效果,而且融合图像能够保留原始图像丰富的边缘纹理和对比度信息。
In order to solve the target ambiguity and information loss in the fusion results of traditional infrared and visible images,a fusion method of infrared and visible images based on the target enhancement and mouse swarm optimization,which is abbreviated as TERSFuse.Firstly,in order to reduce the loss of the original image details in the fusion results,the infrared contrast enhancement module and the visible image enhancement module based on the brightness perception are constructed respectively.Secondly,the infrared and visible enhanced images were decomposed by using the Laplace pyramid transform to obtain the corresponding high and low frequency images.In order to make the fusion result fully retain the original information,the"maximum absolute value"rule is used to fuse the infrared and visible high frequency images,and the low frequency images are fused by calculating the weight coefficient.Finally,the image reconstruction module based on the rat swarm optimization is designed to achieve the adaptive allocation of weight parameters of high frequency and low frequency image reconstruction,and then improve the visual effect of the fused image.In order to verify the advantages of the present algorithm,the experimental results show that the present algorithm not only obtains the good visual effects,but also can retains the rich edge texture and contrast information of the original image.
作者
郝帅
孙曦子
马旭
安倍逸
何田
李嘉豪
孙思雅
HAO Shuai;SUN Xizi;MA Xu;AN Beiyi;HE Tian;LI Jiahao;SUN Siya(School of Electrical and Control Engineering,Xi′an University of Science and Technology,Xi′an 710054,China)
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2024年第4期735-743,共9页
Journal of Northwestern Polytechnical University
基金
中国博士后科学基金(2020M683522)
陕西省自然科学基础研究计划(2024JC-YBMS-490)资助。
关键词
图像融合
红外与可见光图像
多尺度变换
鼠群优化
image fusion
infrared and visible image
multi-scale transform
rat swarm optimization
