光电工程, 2014, 41 (10): 12, 网络出版: 2014-11-06   

基于LNSST与PCNN的红外与可见光图像融合

Fusion Algorithm of Infrared and Visible Images Based on Local NSST and PCNN
作者单位
1 吉林大学 仪器科学与电气工程学院,长春130022
2 ,长春130022
3 吉林大学 汽车仿真与控制国家重点实验室,长春130022
4 长春理工大学 电子信息工程学院,长春 130022
摘要
为了提升红外与可见光图像融合精度,提出了一种局部化非下抽样剪切波变换与脉冲耦合神经网络相结合的红外与可见光图像融合方法。首先,利用局部化非下抽样剪切波对源图像进行多尺度、多方向分解;然后,在分解后的各子带图像中进行块奇异值分解,求取区域特征能量值作为脉冲耦合神经网络对应神经元的链接强度。经过脉冲耦合神经网络点火处理,获取子带图像的点火映射图,通过判决选择算子,选择各子带图像中的明显特征部分生成子带融合图像;最后,应用局部化非下抽样剪切波逆变换重构图像。采用多组红外与可见光图像进行融合实验,并对融合结果进行了客观评价。实验结果表明本文提出的融合方法在主观和客观评价上均优于已有文献的一些典型融合方法,可获得更好的融合效果。
Abstract
For enhancing fusion accuracy of infrared and visible images, an adaptive fusion algorithm of infrared and visible images based on Local Nonsubsampled Shearlet Transform (LNSST) and Pulse Coupled Neural Networks (PCNN)is proposed. First,source images are decomposed to multi-scale and multi-direction subband images by LNSST. Secondly,blocked singular value decomposition of each subband image is done to calculate the area feature energy value which is served as linking strength of each neuron in PCNN. After the processing of PCNN with the adaptive linking strength, new fire mapping images of the entire subband images are obtained, the clear objects of subband images are selected by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a group of new clear subband images. Finally, fused subband images are reconstructed to image by local nonsubsampled shearlet inverse transform. Some fusion experiments on several sets of infrared and visible images are done and objective performance assessments are implemented to fusion results. The experimental results indicate that the proposed method performs better in subjective and objective assessments than a few existing typical fusion techniques in the literatures and obtains better fusion performance.

陈广秋, 高印寒, 段锦, 林杰. 基于LNSST与PCNN的红外与可见光图像融合[J]. 光电工程, 2014, 41(10): 12. CHEN Guangqiu, GAO Yinhan, DUAN Jin, LIN Jie. Fusion Algorithm of Infrared and Visible Images Based on Local NSST and PCNN[J]. Opto-Electronic Engineering, 2014, 41(10): 12.

本文已被 3 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!