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

doi:10.3969/j.issn.1000-1093.2013.07.003

兵工学报 ›› 2013, Vol. 34 ›› Issue (7): 815-820.doi: 10.3969/j.issn.1000-1093.2013.07.003

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

王珺¹, 彭进业¹, 何贵青¹, 冯晓毅¹, 阎昆²

1. 西北工业大学电子信息学院, 陕西西安710072; 2. 中国空间电子信息技术研究院, 陕西西安710000

收稿日期:2012-10-15 修回日期:2012-10-15 上线日期:2014-08-19
作者简介:王珺(1987—),女,博士研究生。
基金资助:
国家自然科学基金项目(61075014、60875016);航天支撑基金项目(2011XW080001C080001);西北工业大学基础研究基金项目(JC20100215);西北工业大学博士论文创新基金项目(CX201318)

Fusion Method for Visible and Infrared Images Based on Non-subsampled Contourlet Transform and Sparse Representation

WANG Jun¹, PENG Jin-ye¹, HE Gui-qing¹, FENG Xiao-yi¹, YAN Kun²

1. School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China; 2. Academy of Space Electronic Information Technology, Xi'an 710000, Shaanxi, China

Received:2012-10-15 Revised:2012-10-15 Online:2014-08-19

摘要/Abstract

摘要： 针对非下采样Contourlet 变换( NSCT) 中低频子带系数稀疏度较低不利于融合的问题,提出基于NSCT 和稀疏表示的图像融合方法。对红外与可见光图进行NSCT 变换;对稀疏度较低的低频子带系数提取共有和特有系数,并按照特有系数的活动水平自适应调整权重融合;对稀疏度较高的高频方向子带系数,采用同一尺度下系数绝对值之和最大的方法进行融合。经NSCT 逆变换后得到融合图像。实验结果表明,与传统基于变换的DWT、NSCT 融合方法以及基于稀疏表示的SOMP、JSR 算法比较,文中方法可以获得更好的融合效果。

关键词: 信息处理技术, 图像融合, 非下采样Contourlet 变换, 稀疏表示, 红外图像, 可见光图像

Abstract: An image fusion method based on NSCT and spare representation is presented for the lowersparseness of low-frequency sub-band coefficients unfavorable to fusion. Firstly, the infrared and visibleimages are transformed by NSCT, the common and innovation coefficients are extracted from the sparsecoefficients of low-frequency sub-band, and the sparse coefficients are adaptively weighted by the specificcoefficients. Secondly, the high-frequency sub-band coefficients with higher sparseness are fused by usinga method which takes the sum of absolute values of its coefficients be maximal at the same scale. Finally,the fusion image is reconstructed by the inverse NSCT. The method has better fusion performance than thetraditional fusion method based on DWT, NSCT, and the SOMP, JSR fusion method based on sparse representation.

Key words: information processing, image fusion, non-subsampled Contourlet transform, sparse representation, infrared image, visible image

中图分类号:

TP391

王珺, 彭进业, 何贵青, 冯晓毅, 阎昆. 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 Jun, PENG Jin-ye, HE Gui-qing, FENG Xiao-yi, YAN Kun. Fusion Method for Visible and Infrared Images Based on Non-subsampled Contourlet Transform and Sparse Representation[J]. Acta Armamentarii, 2013, 34(7): 815-820.

参考文献

[1] Mahyari A G, Yazdi M. Panchromatic and multispectral image fusion based on maximization of both spectral and spatial similarities[J]. IEEE Transactions on Geoscience and Remote Sensing,2011, 49(6): 1-10.

[2] 赵诚, 付梦印, 王立平. 基于粒子群优化的多分辨率图像融合[J]. 兵工学报, 2010,31(2):171-176.

ZHAO Cheng, FU Meng-yin, WANG Li-ping. Multi-resolution image fusion using particle swarm optimization[J]. Acta Armamentarii, 2010,31(2):171- 176. (in Chinese)

[3] Do M N, Vetterli M. The coutourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091- 2106.

[4] 常霞,焦李成,贾建华. 基于非下采样Contourlet 的多传感器图像自适应融合[J]. 计算机学报,2009,32(11):2229-2237.

CHANG Xia, JIAO Li-cheng, JIA Jian-hua, Multisensor image adaptive fusion based on nonsubsampled contourlet[ J]. Chinese Journal of Computers, 2009,32 (11):2229-2237. (in Chinese)

[5] Yang B, Li S T, Pixel-level image fusion with simultaneous orthogonal matching pursuit[J]. Information Fusion, 2012, 1(13):10-19.

[6] Yu N N, Qiu T S, Bi F. Image features extraction and fusion based on joint sparse representation[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5):1074-1082.

[7] Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform: theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006,15(10): 3089-3101.

[8] Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation [ J].IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.

[9] Ophir B, Lustig M, Elad M. Multi-scale dictionary learning using wavelets[J]. IEEE Journal of Selected Topics in Signal Processing, 2011,5(5):1014-1024.

[10] Duarte M F, Sarvotham S, Baron D, et al. Distributed compressed sensing of jointly sparse signals[C]//Computer Society.Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers. US: Institute of Electrical and Electronics Engineers Computer Society, 2005: 1537-1541.

[11] Piella G, Heijmans H, A new quality metric for image fusion[C]

//Proceedings of IEEE International Conference on Image Processing. Barcelona:IEEE,2003:173-176.

[12] Petrovic V. Subjective tests for image fusion evaluation and objective metric validation[J]. Information Fusion, 2007, 8(2):208-216.

[13] Xydeas C S, Petrovic V. Objective image fusion performance measure[J]. Electronics Letters, 2003, 6(4): 308-309.

[1]	李博，王博，韩京冶，杨宗睿，栗霖雲，傅畅之. 基于车载计算机的红外图像移动目标检测[J]. 兵工学报, 2022, 43(S1): 66-73.
[2]	李良福，陈卫东，高强，许开銮，刘轩，何曦，钱钧. 基于深度学习的光电系统智能目标识别[J]. 兵工学报, 2022, 43(S1): 162-168.
[3]	唐伟，贾方秀，王晓鸣. 基于双边滤波的可见光与红外图像自适应融合[J]. 兵工学报, 2022, 43(11): 2836-2845.
[4]	丁贵鹏，陶钢，李英超, 庞春桥，王小峰，段桂茹. 基于非下采样轮廓波变换与引导滤波器的红外及可见光图像融合[J]. 兵工学报, 2021, 42(9): 1911-1922.
[5]	梁杰，李磊，任君，齐航，周红丽. 基于深度学习的红外图像遮挡干扰检测方法[J]. 兵工学报, 2019, 40(7): 1401-1410.
[6]	仇荣超，吕俊伟，宫剑，修炳楠，马新星，刘思彤. 前视红外图像中海岸线与海天线的通用检测方法研究[J]. 兵工学报, 2019, 40(6): 1171-1178.
[7]	刘德鹏，李正周，曾靖杰，熊伟奇，亓波. 基于多尺度局部对比度和多尺度梯度一致性的红外小弱目标检测算法[J]. 兵工学报, 2018, 39(8): 1526-1535.
[8]	曾溢良，蓝金辉，邹金霖. 滑动置信度约束的红外弱小目标跟踪算法研究[J]. 兵工学报, 2017, 38(9): 1771-1778.
[9]	李关防，崔杰，袁富宇. 基于线谱瞬时频率估计的被动声纳目标运动分析[J]. 兵工学报, 2017, 38(7): 1395-1401.
[10]	朱甦，薄煜明，何亮. 基于冗余字典的多特征压缩感知目标跟踪算法[J]. 兵工学报, 2017, 38(6): 1140-1146.
[11]	王啸, 韩太林, 张永立, 刘轩, 王义君, 宫玉琳. 多通道瞬态信号自适应变频算法[J]. 兵工学报, 2017, 38(6): 1161-1167.
[12]	张石，张百海，王飞帆，关子霄. 基于跳数量化的无线传感器网络节点定位算法[J]. 兵工学报, 2017, 38(5): 932-939.
[13]	夏平, 任强, 吴涛, 雷帮军. 融合多尺度统计信息模糊C均值聚类与Markov随机场的小波域声纳图像分割[J]. 兵工学报, 2017, 38(5): 940-948.
[14]	曹俊，郑翠娥，孙大军，张殿伦. 基于凸优化的水下载体定位研究[J]. 兵工学报, 2017, 38(3): 520-526.
[15]	向颉，茅永兴，郭才发. 船载雷达测速数据的船速修正新方法[J]. 兵工学报, 2017, 38(11): 2268-2273.

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

Fusion Method for Visible and Infrared Images Based on Non-subsampled Contourlet Transform and Sparse Representation

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价