局部化NSST与PCNN相结合的图像融合

陈广秋; 高印寒; 才华; 刘广文; 段云鹏

doi:10.3788/YJYXS20153004.0701

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局部化NSST与PCNN相结合的图像融合

成像技术与图像处理 | 更新时间：2020-08-12

- 局部化NSST与PCNN相结合的图像融合
- Image fusion algorithm based on local NSST and PCNN
- 液晶与显示 2015年30卷第4期页码：701-712
- 作者机构：
  
  1. 长春理工大学电子信息工程学院,吉林长春,中国,130022
  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. 北京遥感设备研究所北京,100854
- 作者简介：
- 基金信息：
  
  吉林省科技发展计划(No.20130101179JC)();吉林省公共计算平台资助()
- DOI：10.3788/YJYXS20153004.0701
  中图分类号： TP391.4
- 收稿日期：2014-07-22，
  
  修回日期：2014-10-09，
  
  纸质出版日期：2015-08-05
- 稿件说明：
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陈广秋, 高印寒, 才华, 刘广文, 段云鹏. 局部化NSST与PCNN相结合的图像融合[J]. 液晶与显示, 2015,30(4): 701-712

CHEN Guang-qiu, GAO Yin-han, CAI Hua, LIU Guang-wen, DUAN Yun-peng. Image fusion algorithm based on local NSST and PCNN[J]. , 2015,30(4): 701-712
陈广秋, 高印寒, 才华, 刘广文, 段云鹏. 局部化NSST与PCNN相结合的图像融合[J]. 液晶与显示, 2015,30(4): 701-712 DOI： 10.3788/YJYXS20153004.0701.

CHEN Guang-qiu, GAO Yin-han, CAI Hua, LIU Guang-wen, DUAN Yun-peng. Image fusion algorithm based on local NSST and PCNN[J]. , 2015,30(4): 701-712 DOI： 10.3788/YJYXS20153004.0701.

摘要

为了提升多模态图像融合精度

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

利用局部区域奇异值构造的局部结构信息因子作为PCNN神经元链接强度。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。选用多组不同模态的图像进行实验

并对实验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一些典型融合方法

可获得更好的融合效果。

Abstract

For enhancing fusion accuracy of multi-modality images

an adaptive image fusion algorithm 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

local area singular value decomposition in each subband image is done to construct a local structure information index which is served as linking strength of each neuron in PCNN.After the fire processing of PCNN

new fire mapping images of all the subbands 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 subbands are reconstructed to image by local nonsubsampled shearlet inverse transform.Some fusion experiments on several sets of different modality 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 literature and obtains better fusion performance.

关键词

Keywords

references

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