基于LNSST与PCNN的红外与可见光图像融合

为了提升红外与可见光图像融合精度,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红外与可见光图像融合方法。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进行块奇异值分解,求取区域特征能量值作为脉冲耦合神经网络对应神经元的链接强度。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。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已有文献的一些典型融合方法,可获得更好的融合效果。

Abstract

For enhancing fusion accuracy of infrared and visible images, an adaptive fusion algorithm of infrared and visible images 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,blocked singular value decomposition of each subband image is done to calculate the area feature energy value 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 the entire subband images 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 subband images are reconstructed to image by local nonsubsampled shearlet inverse transform. 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 method performs better in subjective and objective assessments than a few existing typical fusion techniques in the literatures and obtains better fusion performance.

参考文献

[1] 路雅宁,郭雷,李晖晖. 结合边缘特征的遥感图像融合 [J]. 光电工程,2012,39(9):18-23.

LU Yaning,GUO Lei,LI Huihui. Remote Sensing Image Fusion Using Edge Information [J]. Opto-Electronic Engineering,2012,39(9):18-23.

[2] 金炜,符冉迪,叶明,等. 采用双树轮廓波及压缩传感的多聚焦图像融合 [J]. 光电工程,2011,38(4):87-94.

JIN Wei,FU Randi,YE Ming,et al. Multi-focus Image Fusion Using Dual-tree Contourlet and Compressed Sensing [J].Opto-Electronic Engineering,2011,38(4):87-94.

[3] Cunha A L,Zhou J P,Do M N. The Nonsubsampled Contourlet Transform:Theory,Design and Applications [J]. IEEE Trans.Image Proc(S1057-7149),2006,15(10):3089-3101.

[4] Easley G,Labate D,Lim W Q．Sparse directional image representations using the discrete shearlet transform [J]. Applied and Computational Harmonic Analysis(S1063-5203),2008,25(1):25-46．

[5] 申晓华,杨国胜,张焕龙. 改进的基于区域能量的图像融合方法 [J]. 弹箭与制导学报,2006,26(4):279-281.

SHEN Xiaohua,YANG Guosheng,ZHANG Huanlong. Improved on the Approach of Image Fusion Based on Region-energy[J]. Journal of Projectiles; Rockets; Missiles and Guidance,2006,26(4):279-281.

[6] ZHENG Youzhi,HOU Xiaodong,BIAN Tiantian,et al. Effective Image Fusion Rules of Multiscale Image Decomposition [C]// Proceedings of the 5th International Symposium on image and Signal Processing and Analysis,Istanbul,2007:362-366.

[7] 王红梅,陈励华,李言俊,等. 一种基于显著特征的图像融合算法 [J]. 西北工业大学学报,2010,28(4):486-490.

WANG Hongmei,CHEN Lihua,LI Yanjun,et al. A New and More Effective Image Fusion Algorithm Based on Salient Feature[J]. Journal of Northwestern Polytechnical University,2010,28(4):486-490.

[8] 李钢,王雷,张仁斌. 基于特征能量加权的红外与可见光图像融合 [J]. 光电工程,2010,27(3):83-87.

LI Gang,WANG Lei,ZHANG Renbin. Infrared and Visible Image Fusion Based on Feature Energy [J]. Opto-Electronic Engineering,2010,37(3):83-87.

[9] 郭明,符拯,奚晓梁. 基于局部能量的NSCT域红外与可见光图像融合算法 [J]. 红外与激光工程,2012,41(8):2229-2235.

GUO Ming,FU Zheng,XI Xiaoliang. Novel fusion algorithm for infrared and visible images based on local energy in NSCT domain [J]. Infrared and Laser Engineering,2012,41(8):2229-2235.

[10] 童涛,杨桄,谭海峰,等. 基于NSCT 变换的多传感器图像融合算法 [J]. 地理与地理信息科学,2013,29(2):22-25.

TONG Tao,YANG Guang,TAN Haifeng,et al. Multi-Sensor Image Fusion Algorithm Based on NSCT [J]. Geography and Geo-Information Scicnce,2013,29(2):22-25.

[11] 延翔,秦翰林,刘上乾,等. 基于Tetrolet 变换的图像融合 [J]. 光电子激光,2013,24(8):1629-1633.

YAN Xiang , QIN Hanlin , LIU Shangqian , et al. Image fusion based on Tetrolet transform [J]. Journal of Optoelectronics·Laser,2013,24(8):1629-1633.

[12] 郭茂耘,李华锋,柴毅. 提升静态小波与自适应 PCNN 相结合的图像融合算法 [J]. 光电工程,2010,37(12):67-74.

GUO Maoyun,LI Huafeng,CHAI Yi. Image Fusion Using Lifting Stationary Wavelet Transform and Adaptive PCNN [J].Opto-Electronic Engineering,2010,37(12):67-74.

[13] 赵景朝,曲仕茹. 基于Curvelet 变换与自适应 PCNN 的红外与可见光图像融合 [J]. 西北工业大学学报,2011,29(6):849-853.

ZHAO Jingchao,QU Shiru. A Better Algorithm for Fusion of Infrared and Visible Image Based on Curvelet Transform and Adaptive Pulse Coupled Neural Networks(PCNN) [J]. Journal of Northwestern Polytechnical University,2011,29(6):849-853.

[14] 金星,李晖晖,时丕丽. 非下采样Contourlet 变换与脉冲耦合神经网络相结合的SAR 与多光谱图像融合 [J]. 中国图象图形学报,2012,17(9):1188-1195.

JIN Xing,LI Huihui,SHI Pili. SAR and multispectral image fusion algorithm based on pulse coupled neural networks and non-subsampled Contourlet transform [J]. Journal of Image and Graphics,2012,17(9):1188-1195.

[15] 李美丽,李言俊,王红梅,等. 基于自适应脉冲耦合神经网络图像融合新算法 [J]. 光电子激光,2010,21(5):779-782.

LI Meili,LI Yanjun,WANG Hongmei,et al. A new image fusion algorithm based on adaptive PCNN [J]. Journal of Optoelectronics·Laser,2010,21(5):779-782.

[16] 朱亚辉,彭国华. 基于奇异值分解的图像融合效果综合评价研究 [J]. 西北工业大学学报,2013,31(1):25-28.

ZHU Yahui,PENG Guohua. A Novel and Better Performance Evaluation Algorithm for Image Fusion Based on Singular Value Decomposition [J]. Journal of Northwestern Polytechnical University,2013,31(1):25-28.

[17] QU Guihong,ZHANG Dali,YAN Pinfan. Information Measure for Performance of Image Fusion [J]. Electronic Letters(S0013-5194),2002,38(7):313-315.

[18] Wang Z,Bovik A C,Sheik H R,et al. Image Quality Assessment:From error visibility to structural similarity [J]. IEEE Transactions on Image Processing(S1057-7149),2004,13(4):600-612.

[19] Xydeas C S,Petrovi V. Objective Image Fusion Performance Measure [J]. Electronics Letters(S0013-5194),2000,36(4):308-309.

[20] 潘瑜,王静,孙权森,等. 结合图像质量评价参数的多尺度分解融合策略 [J]. 应用科学学报,2011,29(2):159-168.

PAN Yu,WANG Jing,SUN Quansen,et al. Fusion Strategy of Multi-scale Image Decomposition Combined with Image Quality Evaluation [J]. Journal of Applied Sciences,2011,29(2):159-168.

[21] 杨桄,童涛,陆松岩,等. 基于多特征的红外与可见光图像融合 [J]. 光学精密工程,2014,22(2):489-496.

YANG Guang,TONG Tao,LU Songyan,et al. Fusion of infrared and visible images based on multi-features [J]. Optics and Precision Engineering,2014,22(2):489-496.

[22] 郭明,王书满. 基于区域和方向方差加权信息熵的图像融合 [J]. 系统工程与电子技术,2013,35(4):720-724.

GUO Ming,WANG Shuman. Image fusion based on region and directional variance weighted entropy [J]. Systems Engineering and Electronic,2013,35(4):720-724.

陈广秋, 高印寒, 段锦, 林杰. 基于LNSST与PCNN的红外与可见光图像融合[J]. 光电工程, 2014, 41(10): 12. CHEN Guangqiu, GAO Yinhan, DUAN Jin, LIN Jie. Fusion Algorithm of Infrared and Visible Images Based on Local NSST and PCNN[J]. Opto-Electronic Engineering, 2014, 41(10): 12.

基于LNSST与PCNN的红外与可见光图像融合

关于本站 Cookie 的使用提示

全站搜索

基于LNSST与PCNN的红外与可见光图像融合

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索