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1. 吉林大学 仪器科学与电气工程学院,吉林 长春,130061
2. 吉林大学 汽车仿真与控制国家重点实验室,吉林 长春,130022
3. 长春理工大学 电子信息工程学院,吉林 长春,130022
收稿日期:2014-04-18,
修回日期:2014-05-29,
网络出版日期:2014-04-30,
纸质出版日期:2015-02-05
移动端阅览
陈广秋, 高印寒, 段锦, 韩泽宇, 才华. 基于奇异值分解的PCNN红外与可见光图像融合[J]. 液晶与显示, 2015,30(1): 126-136
CHEN Guang-qiu, GAO Yin-han, DUAN Jin, HAN Ze-yu, CAI Hua. Fusion algorithm of infrared and visible images based on singular value decomposition and PCNN[J]. , 2015,30(1): 126-136
陈广秋, 高印寒, 段锦, 韩泽宇, 才华. 基于奇异值分解的PCNN红外与可见光图像融合[J]. 液晶与显示, 2015,30(1): 126-136 DOI: 10.3788/YJYXS20153001.0126.
CHEN Guang-qiu, GAO Yin-han, DUAN Jin, HAN Ze-yu, CAI Hua. Fusion algorithm of infrared and visible images based on singular value decomposition and PCNN[J]. , 2015,30(1): 126-136 DOI: 10.3788/YJYXS20153001.0126.
为了提升红外与可见光图像融合精度
提出了一种基于局部区域奇异值分解的自适应PCNN红外与可见光图像融合算法.利用局部区域奇异值构造局部结构信息因子
作为PCNN对应神经元的链接强度.经过PCNN点火处理
获得源图像的点火映射图
通过比较选择算子
选择源图像中明显特征部分生成融合图像.采用多组红外与可见光图像进行融合实验
并对融合结果进行客观评价.实验结果表明本文提出的算法在主观和客观评价上均优于已有文献的一些典型融合算法
可获得更好的融合效果.
For enhancing fusion accuracy of infrared and visible images
an adaptive fusion algorithm based on local area singular value decomposition (SVD) and pulse coupled neural networks (PCNN) is proposed. Local area singular value decomposition in each source image is done to construct a local structure information index 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 source images are obtained
the clear objects of source 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 new clear image. 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 algorithm performs better in subjective and objective assessments than a few existing typical fusion techniques in the literature and can obtain better fusion performance.
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