基于稀疏表示的WMSN红外和可见光图像融合

J4 ›› 2014, Vol. 36 ›› Issue (05): 957-962.

基于稀疏表示的WMSN红外和可见光图像融合

罗晖，刘洁丽，祁美丽

（华东交通大学信息工程学院，江西南昌 330013）

收稿日期:2013-01-17 修回日期:2013-05-10 出版日期:2014-05-25 发布日期:2014-05-25
基金资助:
国家自然科学基金资助项目（61261040）

Infrared and visible image fusion in WMSN
based on sparse representation

LUO Hui，LIU Jieli，QI Meili

（School of Information Engineering,East China Jiaotong University,Nanchang 330013,China）

Received:2013-01-17 Revised:2013-05-10 Online:2014-05-25 Published:2014-05-25

摘要/Abstract

摘要：

在使用无线多媒体传感网络WMSN进行环境监测的过程中，对同一场景所采集的红外和可见光源图像进行信息融合时，传统的方法融合的数据量较大且没有充分考虑其内在稀疏性和丰富的结构特征，图像融合的质量不高。将稀疏表示理论应用于WMSN红外和可见光图像融合中，在原始DCT冗余字典基础上，结合KSVD字典训练算法和同步正交匹配追踪SOMP算法对WMSN红外和可见光图像进行有效的稀疏表示，并选择自适应加权平均融合规则对稀疏表示系数进行融合处理。仿真结果表明，相对于传统的基于空域及变换域的红外和可见光图像融合方法，该方法更能从WMSN含噪图像中有效地保留源图像的有用信息，获得较好的融合效果。

关键词: 无线多媒体传感器网络, 图像融合, 稀疏表示, 同步正交匹配追踪, 自适应加权平均

Abstract:

When Wireless Multimedia Sensor Network (WMSN) is used for environment detection and the infrared image and the visible image collected from the same scene are fused, the traditional approaches have large amount of fused data and do not fully consider the internal sparsity and the complexity of structure features, so the fusion quality is low.The theory of sparse representation is applied to WMSN infrared and visible image fusion. Based on the original DCT redundant dictionaries, the KSVD training method is combined with the Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm to do effective sparse representation for WMSN infrared images and visible images.And adaptive weighted average fusion rule is selected to deal with the sparse representation coefficients.Experimental results show that,compared with traditional infrared and visible image fusion methods based on spatial and transformed domains, the proposed method can effectively preserve the useful information and get the better fused image.

Key words: WMSN;image fusion;sparse representation;simultaneous orthogonal matching pursuit;adaptive weighted average

罗晖，刘洁丽，祁美丽. 基于稀疏表示的WMSN红外和可见光图像融合[J]. J4, 2014, 36(05): 957-962.

LUO Hui，LIU Jieli，QI Meili. Infrared and visible image fusion in WMSN
based on sparse representation [J]. J4, 2014, 36(05): 957-962.

参考文献

［1］Akyildiz I F,Melodia T,Chowdhury K R.A survey on wireless multimedia sensor networks［J］. Computer Networks, 2007, 51:921960.
［2］Harjito B, Han Song. Wireless multimedia sensor networks applications and security challenges［C］∥Proc of International Conference on Broadband, Wireless Computing, Communication and Applications,2010:842846.
［3］Yang L, Zhou Y, Yang J, et al. Variance WIE based infrared images processing［J］. Electronics Letters, 2006, 42 (15):857859.
［4］Kuang Yan, Wu Yunfeng. Research on image fusion for visible and infrared images［C］∥Proc of the 10th International Conference on Electronic Measurement & Instruments, 2011:7779.
［5］Wan Tao,Qin Zengchang.An application of compressive sensing for image fusion［J］. International Journal of Computer Mathematics, 2011, 88(18):39153930.
［6］Fadili M J, Starck J L, Bobin J，et al. Image decomposition and separation using sparse representations:An overview［J］.Proc of the IEEE, 2010, 98(6):983994.
［7］Aharon M, Elad M, Bruckstein A. KSVD:An algorithm for designing overcomplete dictionaries for sparse representation［J］. IEEE Transactions on Signal Process, 2006, 54(11):43114322.
［8］Jolliffe I T. Principal component analysis［M］. New York:Springer, 2002.
［9］Engan K, Aase S O, Husy J H. Multiframe compression:Theory and design［J］. Signal Process, 2000, 80(10):21212140.
［10］Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries［J］. IEEE Transactions on Image Process, 2006, 15(12):37363745.
［11］Protter M,Elad M.Image sequence denoising via sparse and redundant representations［J］. IEEE Transactions on Image Process, 2009, 18(1):2735.
［12］Yu Nannan, Qiu Tianshuang, Bi Feng, et al. Image features extraction and fusion based on joint sparse representation［J］. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5):10741082.
［13］Tropp J A, Gilbert A C, Strauss M J. Algorithms for simultaneous sparse approximation:Part I:greedy pursuit［J］. Signal Processing, 2006,86 (3):572588.
［14］Li X, Qin SY. Efficient fusion for infrared and visible images based on compressive sensing principle［J］. IET Image Processing, 2011, 5(2):141147.
［15］Liu Zheng, Blasch E, Xue Zhiyun,et al. Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision:A comparative study［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,34(1):94109.

[1]	李一, 李阳, 苗壮, 王家宝, 张睿. 一种扩展VIFB的红外与可见光图像融合基准[J]. 计算机工程与科学, 2022, 44(06): 1072-1082.
[2]	张丽霞, 曾广平, 宣兆成. 多源图像融合方法的研究综述[J]. 计算机工程与科学, 2022, 44(02): 321-334.
[3]	张贵仓, 王静, 苏金凤. 改进稀疏表示与积化能量和的多聚焦图像融合[J]. 计算机工程与科学, 2022, 44(01): 124-131.
[4]	何选森, 徐丽, 夏娟 . 具有相似性传播的K-均值欠定盲分离算法[J]. 计算机工程与科学, 2021, 43(10): 1880-1890.
[5]	张丽霞, 曾广平, 宣兆成. NSST域下SPCNN与SR结合的多源图像融合[J]. 计算机工程与科学, 2020, 42(11): 2042-2049.
[6]	张贵仓, 苏金凤, 拓明秀. DTCWT域的红外与可见光图像融合算法[J]. 计算机工程与科学, 2020, 42(07): 1226-1233.
[7]	王杨1,2，向秀梅1,2，卢嘉1,2，郁振鑫1,2. 基于双目融合的无参考立体图像质量评价[J]. 计算机工程与科学, 2020, 42(03): 510-516.
[8]	苏金凤，张贵仓，汪凯. 图像差与加权核范数最小化的压缩图像融合[J]. 计算机工程与科学, 2019, 41(10): 1785-1794.
[9]	李燕,章玥. 基于随机投影与加权稀疏表示残差的光照鲁棒人脸识别方法[J]. 计算机工程与科学, 2018, 40(11): 2015-2022.
[10]	王健1,2,张修飞1,任萍1,院文乐1. 基于增补小波变换和PCNN的NSCT域图像融合算法[J]. 计算机工程与科学, 2018, 40(10): 1822-1828.
[11]	王威1,2，张佳娥1,2. 基于引导滤波和shearlet稀疏的遥感图像融合算法[J]. 计算机工程与科学, 2018, 40(08): 1453-1458.
[12]	王威1,2，张佳娥1,2. 基于引导滤波和shearlet稀疏的遥感图像融合算法[J]. 计算机工程与科学, 2018, 40(07): 1250-1255.
[13]	杨培1，高雷阜1，王江1，訾玲玲2. 基于稀疏表示与字典学习的彩色图像去噪算法[J]. 计算机工程与科学, 2018, 40(05): 842-848.
[14]	殷俊1,2，杨万扣3. 基于局部表示的分类方法及其人脸识别应用[J]. 计算机工程与科学, 2018, 40(03): 500-506.
[15]	张海燕1,张立毅1,2，孙云山1,2. 基于离散剪切波正则化的低剂量CT图像统计重建算法[J]. 计算机工程与科学, 2018, 40(01): 86-92.