[1]王建,吴锡生.基于改进的稀疏表示和PCNN的图像融合算法研究[J].智能系统学报,2019,14(05):922-928.[doi:10.11992/tis.201805045]
 WANG Jian,WU Xisheng.Image fusion based on the improved sparse representation and PCNN[J].CAAI Transactions on Intelligent Systems,2019,14(05):922-928.[doi:10.11992/tis.201805045]
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基于改进的稀疏表示和PCNN的图像融合算法研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年05期
页码:
922-928
栏目:
出版日期:
2019-09-05

文章信息/Info

Title:
Image fusion based on the improved sparse representation and PCNN
作者:
王建 吴锡生
江南大学 物联网工程学院, 江苏 无锡 214122
Author(s):
WANG Jian WU Xisheng
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
关键词:
图像处理图像融合非下采样剪切波变换稀疏表示自适应学习字典联合字典脉冲耦合神经网络改进的空间频率
Keywords:
image processingimage fusionNSSTsparse representationadaptive learning dictionaryjoint dictionaryPCNNimproved spatial frequency
分类号:
TP391.2
DOI:
10.11992/tis.201805045
摘要:
为提高图像融合的清晰度,本文提出一种基于改进的稀疏表示和脉冲耦合神经网络(pulse coupled neural network,PCNN)的图像融合。利用非下采样剪切波变换(non-subsampled shearlet transform,NSST)对源图像进行分解变换,得到相应的低频子带和高频子带具有不同的信息。对于低频子带,采用改进的稀疏表示进行融合,利用K奇异值分解(K-singular value decomposition,K-SVD)算法,并对源图像进行自适应学习的多个子字典构造成联合词典。对于高频子带,则改进PCNN融合系数的选择方法,利用改进的空间频率作为神经元反馈输入来激励PCNN模型,并根据点火输出的总幅度最大的融合规则选择高频系数。最后,将融合后的低频子带和高频子带系数进行NSST逆变换,重构出融合图像。实验结果表明:该算法很好地保留了图像的边缘信息,并且得到的图像在相关的客观评价标准上也取得了良好的效果,表明了本算法的有效性。
Abstract:
To improve the clarity of image fusion, in this paper, we propose an image-fusion algorithm based on improved sparse representation and a pulse-coupled neural network (PCNN). First, using a non-subsampled shearlet transform (NSST), source images are decomposed into low-frequency and high-frequency sub-band coefficients, which contain different information. Then, we use the K-singular value decomposition algorithm to fuse the improved sparse representation with low-frequency sub-band coefficients and construct a joint dictionary from the adaptive learning multiple sub-dictionaries in the source images. The high-frequency sub-band coefficients are fused with the improved PCNN. To stimulate the PCNN model, we use the modified spatial frequency as neuron feedback input. The high-frequency coefficients are selected according to the fusion rule for the maximum amplitude of fire output. Finally, we reconstruct the fused image with the NSST inverse transform of the fused low-frequency and high-frequency sub-band coefficients. The experimental results show that the proposed algorithm preserves the edge information of the source images very well; additionally, the fused image achieves good results on the evaluation criteria, thus verifying the effectiveness of the proposed algorithm.

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备注/Memo

备注/Memo:
收稿日期:2018-05-29。
基金项目:国家自然科学基金项目(61672265).
作者简介:王建,男,1992年生,硕士研究生,主要研究方向为图像融合;吴锡生,男,1959年生,教授,博士,主要研究方向为图像处理和模式识别。曾获江苏省科技进步三等奖2次,中国纺织协会和无锡市科技进步奖3次,软件著作权授权1项,发明专利授权3项。发表学术论文40余篇。
通讯作者:吴锡生.E-mail:wxs@jiangnan.edu.cn
更新日期/Last Update: 1900-01-01