摘要
单一图像无法全面描述目标的信息,实际应用价值低,针对当前红外与可见光图像融合方法存在的一些不足,如:融合质量差等,为了获得更加理想的红外与可见光图像融合效果,提出了基于特征相似性的红外与可见光图像融合方法。首先分析当前红外与可见光图像融合的研究进展,指出各种方法的局限性,然后采用红外图像和可见光图像,并对它们进行图像去噪、增强处理,采用卷积神经网络提取红外与可见光图像的特征,最后根据特征相似性进行红外与可见光图像融合,并对红外与可见光图像融合效果进行了测试,结果表明,本方法提升了红外与可见光图像融合质量,融合效果要明显优于其他红外与可见光图像融合方法。
A single image cannot fully describe the information of the target and has low practical application value.In view of some shortcomings of the current infrared and visible image fusion methods,such as poor fusion quality,in order to obtain a more ideal infrared and visible image fusion effect,an infrared and visible image fusion method based on feature similarity is proposed.First of all,the research progress of infrared and visible image fusion is analyzed,and the limitations of various methods are pointed out.Then,infrared and visible images are used,and image denoising and enhancement processing are carried out.The features of infrared and visible images are extracted by convolution neural network.Finally,infrared and visible image fusion is carried out according to the feature similarity,and the fusion effect of infrared and visible images is tested.The results show that,this method improves the quality of infrared and visible image fusion,and the fusion effect is significantly better than other infrared and visible image fusion methods.
作者
秦伟
段俊阳
QIN Wei;DUAN Junyang(Tongren University,Tongren Guizhou 554300,China)
出处
《激光杂志》
CAS
北大核心
2024年第2期119-123,共5页
Laser Journal
基金
贵州省科技厅基础研究计划(No.ZK[2022]588)
铜仁学院一流本科教育专项项目(No.YLBK-2022019)。
关键词
卷积神经网络
红外图像
可见光图像
图像融合
图像质量
convolution neural network
infrared image
visible light image
image fusion
image quality
