[1]巩稼民,薛孟乐,任帆,等.基于邻域特征与SCM相结合的红外与可见光图像融合[J].红外技术,2018,40(11):1091-1097.[doi:10.11846/j.issn.1001_8891.201811013]
 GONG Jiamin,XUE Mengle,REN Fan,et al.Infrared and Visible Image Fusion Based on Neighborhood Feature and SCM[J].Infrared Technology,2018,40(11):1091-1097.[doi:10.11846/j.issn.1001_8891.201811013]
点击复制

基于邻域特征与SCM相结合的红外与可见光图像融合
分享到:

《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
40
期数:
2018年第11期
页码:
1091-1097
栏目:
出版日期:
2018-11-21

文章信息/Info

Title:
Infrared and Visible Image Fusion Based on Neighborhood Feature and SCM
文章编号:
1001-8891(2018)11-1091-07
作者:
巩稼民薛孟乐任帆丁哲李思平侯玉洁蔡庆
西安邮电大学 电子工程学院
Author(s):
GONG JiaminXUE MengleREN FanDING ZheLI SipingHOU YujieCAI Qing
Xi’an University of Posts and Telecommunications, School of Electronics Engineering
关键词:
红外图像可见光图像图像融合非下采样剪切波变换脉冲皮层发放模型
Keywords:
infrared imagevisible imageimage fusionNSSTSpiking Cortical model
分类号:
TP391
DOI:
10.11846/j.issn.1001_8891.201811013
文献标志码:
A
摘要:
提出了一种基于邻域特征与脉冲发放皮层模型(Spiking Cortical Model,SCM)相结合的红外与可见光图像融合算法。首先,源图像经过非下采样剪切波变换(Non-subsampled Shearlet Transform,
NSST)分解得到各自的低频子带图像和高频子带图像。然后,根据低频与高频子带的特点,选择适合的邻域特征作为SCM的外部激励,通过比较SCM的输出对低频与高频分量进行融合。最后将融合得到的低频与高频子带图像经逆变换重建得到最终的融合图像。通过实验仿真与其他几种方法进行比较,可以看出本文算法的融合图像红外目标突出,背景信息丰富,视觉效果良好,并且在标准差、信息熵,以及互信息等客观评价方面具有明显的提高。

Abstract:
A fusion algorithm of infrared and visible image is proposed based on neighborhood feature and the spiking cortical model(SCM). First, the source images are decomposed using the non-subsampled shearlet transform to get their own low-and high-frequency sub-band images. Then, according to the characteristics of the low-frequency and high-frequency sub-bands, suitable neighborhood features are chosen as the external excitation of SCM and both frequency components are fused by comparing the output of SCM. Finally, the low-frequency and high-frequency sub-band images are reconstructed by inverse transformation to obtain the final fusion image. A comparison with several other image fusion methods demonstrates that our proposed algorithm has an outstanding infrared target, rich background information, and good visual effect. Moreover, it is advantageous in objective evaluation parameters such as standard deviation, information entropy, and mutual information.

参考文献/References:

[1] WU W, QIU Z, ZHAO M, et al. Visible and infrared image fusion using NSST and deep Boltzmann machine[J]. Optik, 2018, 157: 334-342.
[2] KONG S G, HEO J, Boughorbel F, et al. Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition[J]. International Journal of Computer Vision, 2007, 71(2): 215-233.
[3] 陈清江, 张彦博, 柴昱洲, 等. 有限离散剪切波域的红外可见光图像融合[J]. 中国光学, 2016, 9(5): 523-531.??
CHEN Qingjiang, ZHANG Yanbo, CHAI Yuzhou, et al. Fusion of infrared and visible images based on finite discrete shearlet domain[J]. Chinese Optics, 2016, 9(5): 523-531.
[4] Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.?
[5] Cunha A L D, Zhou J, Do M N. The Nonsubsampled Contourlet Transform: Theory, Design, and Applications[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.?
[6] Easley G, Labate D, Lim W-Q. Sparse directional image representations using the discrete shearlet transform[J]. Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46.
[7] KONG W, ZHANG L, LEI Y. Novel fusion method for visible light and infrared images based on NSST-SF-PCNN[J]. Infrared Physics & Technology, 2014, 65: 103-112.
[8] XU X, SHAN D, WANG G, et al. Multimodal medical image fusion using PCNN optimized by the QPSO algorithm[J]. Applied Soft Computing, 2016, 46: 588-595.
[9] LIU Z, FENG Y, ZHANG Y, et al. A fusion algorithm for infrared and visible images based on RDU-PCNN and ICA-bases in NSST domain[J]. Infrared Physics & Technology, 2016, 79: 183-190.
[10] Kanghui Guo D L. Sparse multidimensional representation using shearlets[J]. SIAM Journal on Mathematical Analysis, 2007, 39(1): 11.
[11] 邢笑雪. 基于NSST的图像融合算法研究[D]. 长春: 吉林大学, 2014. XING Xiaoxue. Research on the fusion algorithm based on Non-sumsampled Shearlet Transform[D]. Changchun: Jilin University, 2014.
[12] R Eckhorn, H J Reitboeck, M Arndt, et al. Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex[J]. Neural Computation, 1990, 2(3): 293-307.
[13] J L Johnson, M L Padgett. PCNN models and applications[J]. IEEE Trans. on Neural Networks, 1999, 10(3): 480-498.
[14] ZHAN K, ZHANG H, MA Y. New Spiking Cortical Model for Invariant Texture Retrieval and Image Processing[J]. IEEE Transactions on Neural Networks, 2009, 20(12): 1980-1986.
[15] 绽琨. 脉冲发放皮层模型及其应用[D]. 兰州: 兰州大学, 2010.
ZHAN Kun. Spiking Cortical Model and its applications[D]. Lanzhou: Lanzhou University, 2010.
[16] 王念一. 脉冲发放皮层模型图像融合技术研究[D]. 兰州: 兰州大学, 2014.?
WANG Nianyi. Spiking Cortical Model for image fusion[D]. Lanzhou: Lanzhou University, 2014.
[17] Guihong Q, Dali Z, Pingfan Y. Information measure for performance of image fusion[J]. Electronics Letters, 2002, 38(7): 313-315.
[18] GONG J, WANG B, QIAOL, et al. Image Fusion Method Based on Improved NSCT Transform and PCNN Model [C]//Computational Intelligence and Design (ISCID), 9th International Symposium on IEEE, 2016: 28-31.

相似文献/References:

[1]郭水旺,王宝红,季钢,等.基于基因表达式编码算法的红外图像轮廓提取[J].红外技术,2013,35(01):038.
 GUO Shui-wang,WANG Bao-hong,JI Gang,et al. Infrared Image Contour Extraction Based on the Gene Expression Coding Algorithm[J].Infrared Technology,2013,35(11):038.
[2]路建方,王新赛,肖志洋,等. 基于FPGA的红外图像自适应分段线性增强算法[J].红外技术,2013,35(02):102.
 LU Jian-fang,WANG Xin-sai,XIAO Zhi-yang,et al. An Adaptive Piecewise Linear Enhance Algorithm for Infrared Image Based on FPGA[J].Infrared Technology,2013,35(11):102.
[3]徐铭蔚,李郁峰,陈念年,等.多尺度融合与非线性颜色传递的微光与红外图像染色[J].红外技术,2012,34(12):722.
 XU Ming-wei,LI Yu-feng,CHEN Nian-nian,et al. Coloration of the Low Light Level and Infrared Image Using Multi-scale Fusion and Nonlinear Color Transfer Technique[J].Infrared Technology,2012,34(11):722.
[4]张红辉,罗海波,余新荣,等. 改进的神经网络红外图像非均匀性校正方法[J].红外技术,2013,35(04):232.
[5]张强,侯宁,刘红燕. 红外焦平面阵列非均匀性多点实时压缩校正研究[J].红外技术,2012,34(10):593.
 ZHANG Qiang,HOU Ning,LIU Hong-yan. Study on Real-time Multi-points Compressive Nonuniformity Correction of IRFPA[J].Infrared Technology,2012,34(11):593.
[6]路建方,王新赛,肖志洋,等. 基于灰度分层的FPGA红外图像伪彩色实时化研究[J].红外技术,2013,35(05):285.
 LU Jian-fang,WANG Xin-sai,XIAO Zhi-yang,et al. The Research on Real-time Pseudo-color of Infrared Image in FPGA Based on Gray Delaminating[J].Infrared Technology,2013,35(11):285.
[7]陈钱.红外图像处理技术现状及发展趋势[J].红外技术,2013,35(06):311.
 CHEN Qian.The Status and Development Trend of Infrared Image Processing Technology[J].Infrared Technology,2013,35(11):311.
[8]谭东杰,张安.基于局部直方图规定化的红外图像非均匀性校正[J].红外技术,2013,35(06):325.
 TAN Dong-jie,ZHANG An.Non-uniformity Correction Based on Local Histogram Specification[J].Infrared Technology,2013,35(11):325.
[9]杨悦,刘兴淼,郭启旺,等.基于改进互信息的红外目标匹配跟踪算法[J].红外技术,2013,35(06):350.
 YANG Yue,LIU Xing-miao,GUO Qi-wang,et al.Infrared Object Matching Tracking Algorithm Based on Improved Mutual Information[J].Infrared Technology,2013,35(11):350.
[10]田毅龙,李志军,王卫华,等.基于双核判决的红外小目标检测方法[J].红外技术,2012,34(07):398.
 TIAN Yi-long,LI Zhi-jun,WANG Wei-hua,et al.An Infrared Small Target Detecting Method Based on Two-Core Judging[J].Infrared Technology,2012,34(11):398.
[11]孙爱平,皮冬明,安长亮,等. 光机装校阶段红外与可见光图像配准技术研究[J].红外技术,2013,35(01):050.
 SUN Ai-ping,PI Dong-ming,AN Chang-liang,et al. Study on IR/Visible Image Registration for Lens Assembly[J].Infrared Technology,2013,35(11):050.
[12]纪利娥,杨风暴,王志社,等. 基于边缘图像和SURF特征的可见光与红外图像的匹配算法[J].红外技术,2012,34(11):629.
 JI Li-e,YANG Feng-bao,WANG Zhi-she,et al.Visible and Infrared Image Matching Algorithm Based on Edge Image and SURF Features[J].Infrared Technology,2012,34(11):629.
[13]刘思汝,杨风暴,陈磊.基于关键帧提取的红外与可见光序列图像快速融合[J].红外技术,2014,36(5):350.[doi:10.11846/j.issn.1001_8891.201405002]
 LIU Si-ru,YANG Feng-bao,CHEN-Lei.The Fast Fusion for Infrared and Visible Sequences Image Based on Key Frames Extraction[J].Infrared Technology,2014,36(11):350.[doi:10.11846/j.issn.1001_8891.201405002]
[14]陈木生.基于区域能量比的红外与可见光图像融合方法[J].红外技术,2008,30(四):221.
 CHEN Mu-sheng.Image Fusion Algorithm for Visual and Infrared Image Based on Local Energy Ratio[J].Infrared Technology,2008,30(11):221.
[15]张耀军,吴桂玲,栗 ?磊.基于ST与量子理论模型的红外与可见光图像融合[J].红外技术,2015,37(五):418.[doi:10.11846/j.issn.1001_8891.201505011]
 ZHANG Yao-jun,WU Gui-ling,LI Lei.Fusion for Infrared and Visible Light Images Based on Shearlet Transform and Quantum Theory Model[J].Infrared Technology,2015,37(11):418.[doi:10.11846/j.issn.1001_8891.201505011]
[16]严敏,杨智雄,余春超,等.基于CPCT的彩色图像融合算法[J].红外技术,2015,37(七):566.[doi:10.11846/j.issn.1001_8891.201507005]
 YAN Min,YANG Zhi-xiong,YU Chun-chao,et al.Color Image Fusion Algorithm Based On CPCT[J].Infrared Technology,2015,37(11):566.[doi:10.11846/j.issn.1001_8891.201507005]
[17]董建敏,胡志宇.芯片的红外与可见光图像配准方法研究[J].红外技术,2015,37(增刊1):038.[doi:10.11846/j.issn.1001_8891.2015S1006]
 DONG Jian-min,HU Zhi-yu.Infrared and Visible Image Registration for Chips[J].Infrared Technology,2015,37(11):038.[doi:10.11846/j.issn.1001_8891.2015S1006]
[18]王 聪,钱 晨,孙 伟,等.基于SCM和CST的红外与可见光图像融合算法[J].红外技术,2016,38(5):396.[doi:10.11846/j.issn.1001_8891.201605007]
 WANG Cong,QIAN Chen,SUN Wei,et al.Infrared and Visible Images Fusion Based on SCM and CST[J].Infrared Technology,2016,38(11):396.[doi:10.11846/j.issn.1001_8891.201605007]
[19]吴冬鹏,毕笃彦,马时平,等.边缘和对比度增强的NSST域红外与可见光图像融合[J].红外技术,2017,39(4):358.[doi:10.11846/j.issn.1001_8891.201704011]
 WU Dongpeng,BI Duyan,MA Shiping,et al.Infrared and Visible Image Fusion Based on Improved Saliency Map in NSST Domain [J].Infrared Technology,2017,39(11):358.[doi:10.11846/j.issn.1001_8891.201704011]
[20]肖中杰.基于NSCT红外与可见光图像融合算法优化研究[J].红外技术,2017,39(12):1127.[doi:10.11846/j.issn.1001_8891.20170010]
 XIAO Zhongjie.Improved Infrared and Visible Light Image Fusion Algorithm Based on NSCT[J].Infrared Technology,2017,39(11):1127.[doi:10.11846/j.issn.1001_8891.20170010]

备注/Memo

备注/Memo:
收稿日期:2018-03-30;修订日期:2018-06-25.
作者简介:巩稼民(1962-),男,教授,主要研究方向为光通信技术。E-mail:13289388729@163.com
基金项目:西安邮电大学研究生创新基金项目(CXJJ2017051)。

更新日期/Last Update: 2018-11-20