红外技术2024，Vol.46Issue(8)：892-901,10.
基于快速联合双边滤波器和改进PCNN的红外与可见光图像融合
Infrared and Visible Image Fusion Based on Fast Joint Bilateral Filtering and Improved PCNN
摘要
Abstract
To address the problems of detail loss,inconspicuous targets,and low contrast in infrared and visible image fusion,a fusion method combining fast joint bilateral filtering(FJBF)and an improved pulse-coupled neural network(PCNN)was proposed.The operational efficiency can be effectively improved by ensuring the quality of the fused image.First,the source images were decomposed by fast joint bilateral filtering.Second,to extract significant structure and target information,a weighted average fusion rule based on a visual saliency graph(VSM)was adopted for the basic layer image,and an improved pulse-coupled neural network model was adopted for the detail layer image.All parameters of the PCNN can be adjusted according to the input bands,and the fusion image was reconstructed using the superimposed fusion map of the base layer and the fusion map of the detail layer.The experimental results show that this method can significantly improve the image fusion effect and effectively retain important information,such as targets,background details,and edges.关键词
图像处理/快速联合双边滤波器/脉冲耦合神经网络/红外与可见光图像/图像融合Key words
image processing/fast joint bilateral filter/pulse coupled neural network/infrared and visible image/image fusion分类
计算机与自动化引用本文复制引用
杨艳春,雷慧云,杨万轩..基于快速联合双边滤波器和改进PCNN的红外与可见光图像融合[J].红外技术,2024,46(8):892-901,10.基金项目
长江学者和创新团队发展计划资助(IRT_16R36) （IRT_16R36）
国家自然科学基金(62067006) （62067006）
甘肃省科技计划项目(18JR3RA104) （18JR3RA104）
甘肃省高等学校产业支撑计划项目(2020C-19) （2020C-19）
兰州市科技计划项目(2019-4-49) （2019-4-49）
甘肃省自然科学基金项目(23JRRA847、21JR7RA300) （23JRRA847、21JR7RA300）
兰州交通大学—天津大学联合创新基金项目(2021052). （2021052）
