Visible and Infrared Image Fusion Using Low-Frequency Coefficients Mapping Fusion Rule in the LWT

摘要：

　　In order to improve the effect of detect object in visible and infrared fusion image,a Low-Frequency Coefficients Mapping fusion rule is proposed.The overall fusion scheme based on lifting wavelet transforms.Firstly,the source images of same scene are decomposed using lifting wavelet transform(LWT).Secondly,a Low-Frequency Coefficients Mapping fusion rule is used to select low frequency lifting wavelet coefficients of the visible and infrared images.The fusion rule of local square maximum is used to combine corresponding high frequency coefficients.After fusing low and high frequency coefficients of the source images,the final fused image is obtained using the inverse LWT.The experiments show that the proposed Low Frequency Coefficients Mapping Fusion Rule in the LWT obtains a good fusion results as compared to previous image fusion rule in the LWT such as local energy weighted average and average gradient maxima.

关键词： Image Fusion Lifting Wavelet Transform Fusion Rules Local Square Maximum Weighted Average Low Frequency Coefficients Mapping

作者: Xiaowei Wang Guojun Lai XuDong Wang wenlong Hao

作者单位: Research Center of Unmanned Aerial Vehicle,Army Aviation Institute,Beijing,China Departmet of Command,Army Aviation Institute,Beijing,China

会议类型: 国际会议

会议名称: The 3rd International Conference on Intelligent Energy and Power Systems (IEPS 2017)(第三届智能能源和电力系统国际会议)

会议地点: 杭州

会议语种:英文

页码: 119-125

在线出版日期: 2017-04-15（万方平台首次上网日期，不代表论文的发表时间）