多尺度域内改进模糊规则的红外与可见光图像融合

冯鑫; 胡开群

doi:10.3788/OPE.20152313.0623

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多尺度域内改进模糊规则的红外与可见光图像融合

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- 多尺度域内改进模糊规则的红外与可见光图像融合
- Fusion of infrared and visible images based on improved fuzzy rules in multi-scale domain
- 光学精密工程 2015年23卷第10z期页码：622-629
- 作者机构：
  
  重庆工商大学制造装备机构设计与控制重庆市重点实验室重庆市国际科技合作基地装备系统服役健康保障国际联合研究中心, 重庆 400067
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51375517)();重庆高校创新团队资助项目(No.KJTD101313)();重庆市教委自然科学基金资助项目(No.KJ14006
- DOI：10.3788/OPE.20152313.0623
  中图分类号： TP391
- 收稿日期：2015-05-28，
  
  修回日期：2015-06-12，
  
  纸质出版日期：2015-11-14
- 稿件说明：
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冯鑫, 胡开群,. 多尺度域内改进模糊规则的红外与可见光图像融合[J]. 光学精密工程, 2015,23(10z): 622-629

FENG Xin, HU Kai-qun,. Fusion of infrared and visible images based on improved fuzzy rules in multi-scale domain[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 622-629
冯鑫, 胡开群,. 多尺度域内改进模糊规则的红外与可见光图像融合[J]. 光学精密工程, 2015,23(10z): 622-629 DOI： 10.3788/OPE.20152313.0623.

FENG Xin, HU Kai-qun,. Fusion of infrared and visible images based on improved fuzzy rules in multi-scale domain[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 622-629 DOI： 10.3788/OPE.20152313.0623.

摘要

提出一种平移不变Shearlet域内改进模糊化规则的图像融合方法。首先分别对红外与可见光图像进行平移不变Shearlet变换获取高频和低频子带系数。然后

根据红外图像特性采用局部区域信息熵规则融合低频子带系数;针对Shearlet变换框架冗余特性

融合高频子带系数。为有效解决高频系数冗余信息

分别计算红外与可见光图像高频子带系数图模糊化后的隶属度、非隶属度和模糊度以及最优熵

将获取的模糊化系数图分块并分别根据黑白度获取混合图像

然后重构系数块图像并进行解模糊操作以获取融合后的高频子带系数。最后

通过平移不变Shearlet反变换得到最终的红外与可见光图像融合结果。实验结果表明

本方法融合结果边缘保持度超过0.85

消除了吉布斯现象

有较好的融合效果。

Abstract

On the basis of shift-invariant Shearlet domain fuzzy processing

an image fusion method with improved fuzzy rules was proposed. First of all

infrared and visible light images were processed by Shift Invariant Shearlet Transformation(SIST)to decomposed into low-pass and high-pass subbands. For low frequency subband coefficients

the rules of local area information entropy was used. For high frequency subband coefficients

the Shearlet transform frame redundancy was considered. To solve the redundant information of the high frequency subbands

the infrared and visible light image membership degree

non-belongingness degree

the hesitation degree of high-frequency sub-band figure and the optimal entropy were calculated. The two coefficient images were decomposed

then the total count of blackness

whiteness of two corresponding blocks are computed. Finally

the block of the blended coefficient image was constructed and the infrared and visible light images were obtained by using the SIST. This method effectively eliminates the Gibbs phenomenon

and offers an edge keeping degree more than 0.85.

关键词

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references

LI H H, GUO L, LIU K. SAR and optical image fusion based on curvelet transform[J]. Journal of Optoelectronics Laser, 2009,20(8):1110-1113.

刘卫,殷明,栾静,等. 基于平移不变剪切波变换的域图像融合算法[J]. 光子学报,2013,429(4):496-503. LIU W, YIN M, LUAN J,et al.. Image fusion algorithm based on shift-invariant shearlet transform[J]. Acta Photonica Sinica, 2013,429(4):496-503.(in Chinese)

叶传奇,王宝树,苗启广. 一种基于区域特征的红外与可见光图像融合算法[J]. 光子学报,2009,38(6):1498-1503. YE CH Q, WANG B SH, MIAO Q G. Fusion algorithm of infrared and visible images based on region feature[J]. Acta Photonica Sinica, 2009,38(6):1498-1503.(in Chinese)

刘哲,顾淑音,南炳炳. 一种基于块稀疏贝叶斯学习的压缩图像融合算法[J]. 光子学报, 2013,42(11):1365-1369. LIU ZH, GU SH Y, NAN B B. A compressive image fusion algorithm based on block sparse Bayesian learning[J]. Acta Photonica Sinica, 2013,42(11):1365-1369.(in Chinese)

王珺,彭进业,何贵青. 基于非下采样Contourlet变换和稀疏表示的红外与可见光图像融合方法[J]. 兵工学报,2013,34(7):815-820. WANG J, PENG J Y, HE G Q. Fusion method for visible and infrared images based on non-subsampled Contourlet transform and sparse representation[J]. Acta Armamentarll, 2013,34(7):815-820.(in Chinese)

甘甜,冯少彤,聂守平. 基于分块DCT 变换编码的小波域多幅图像融合算法[J]. 物理学报,2011,60(11):114-120. GAN T, FENG SH T, NIE SH P. Image fusion algorithm based on block DCT in wavelet domain[J]. Acta Phys.Sin, 2011,60(11):114-120.(in Chinese)

KONG W, LEI Y, LEI Y. Fusion technique for grey-scale visible light and infrared images based on non-subsampled Contourlet transform and intensity hue saturation transform[J]. IET Signal Processing, 2011,5(1):75-80.

沈瑜,党建武,冯鑫. 基于Tetrolet 变换的红外与可见光融合[J].光谱学与光谱分析, 2013,33(6):1506-1511. SHEN Y, DANG J W, FENG X. Infrared and visible images fusion based on tetrolet transform[J].Spectroscopy and Spectral Analysis, 2013,33(6):1506-1511.(in Chinese)

郑虹,郑晨,闫秀生. 基于剪切波变换的可见光与红外图像融合算法[J]. 仪器仪表学报, 2012,33(7):1613-1619. ZHENG H, ZHENG CH, YAN X SH. Visible and infrared image fusion algorithm based on shearlet transform[J]. Chinese Journal of Scientific Instrument, 2012,33(7):1613-1619.(in Chinese)

CUNHA A L, ZHOU J P, DO M N. The non-subsampled contourlet transform:theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006,15:3089-3101.

EASLEY G, LABATE D, LIM W Q. Sparse directional image representations using the discrete shearlet transform[J]. Applied and Computational Harmonic Analysis, 2008,25:25-46.

TANASSOV KT, STOEVA S. Intuitionistic fuzzy set, in:Proc.Polish Symp[J]. Interval Fuzzy Math., Poznan, 1993:23-26.

SZMIDT E, KACPRYZYK J. Distance between intuitionistic fuzzy set[J]. Fuzzy Sets Syst, 2000, 114(3):505-518.

VLACHOS I K, DSERGIADIS G. The role of entropy in intuitionistic fuzzy contrast enhancement[J]. Lecture Notes in Artificial Intelligence, Springer, 2007, 4529:104-113.

CHAIRA T. A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set[J]. Appl. Soft Comput, 2012, 12(4):1259-1266.

张强,郭宝龙. 一种基于非下采样Contourlet变换红外图像与可见光图像融合算法[J]. 红外与毫米波学报, 2007,26(6):476-480. ZHANG Q, GUO B L. Fusing of infrared and visible light images based on nonsubsampled Contourlet transform[J]. Journal of Infrared and Millimeter Waves, 2007,26(6):476-480.(in Chinese)

LI X, QIN SY. Efficient fusion for infrared and visible images based on compressive sensing principle[J]. IET Image Process, 2011, 5(2):141-147.

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