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基于联合稀疏表示的红外与可见光图像融合研究

时间:2018-12-02 13:39来源:毕业论文
采用K-SVD算法训练字典,继而利用 OMP 优化算法求解稀疏系数,包括共有和特有稀疏系数,再运用加权平均规则融合特有系数,最后利用融合系数和过完备字典重构图像

摘要红外与可见光图像融合是图像融合领域的重要分支,其在航空、遥感等军民领域具有广泛的应用前景。为了找出适合红外与可见光图像的融合算法,本文从图像的联合稀疏性出发,对图像融合算法进行深入研究。 本文在对图像融合基本原理进行系统研究的基础上,结合目前性能优越的稀疏表示理论, 重点研究了一种基于联合稀疏表示的红外与可见光图像融合算法。首先采用K-SVD算法训练字典,继而利用 OMP 优化算法求解稀疏系数,包括共有和特有稀疏系数,再运用加权平均规则融合特有系数,最后利用融合系数和过完备字典重构图像。实验的结果显示,本文所采用的融合方式其融合结果红外目标突出,背景纹理清晰,且具备去噪声的功能。   30902
毕业论文关键词  红外与可见光图像融合  联合稀疏表示  共有和特有稀疏系数 K-SVD OMP  
Title  Fusion Research of Infrared and Visible Images Based on Joint Sparse Representation
Abstract Fusion of infrared and visible images is an important branch of image fusion field, which has wide applications in  military and civilian fields such as aviation, remote sensing and so on. To find a suitable fusion algorithm for infrared and visible images,  set out  from the images’ joint sparsity, the image fusion algorithm will be further research in this paper. Based  on the system study on basic principle of the image fusion, combined with the sparse representation theory which has superior performance in current,  this paper focuses  on a joint  sparse representation-based image fusion method for   infrared and visible images. Firstly, train the over-completed dictionary by K-SVD algorithm, then solve the sparse coefficients with optimization algorithm, OMP, the coefficients include common sparse coefficient and unique sparse coefficients, then use the weighted average rule fuse  unique factors, finally, reconstruct the fuse image by fusion coefficient and over-complete dictionary. Experimental result shows that the fusion method in this paper can obtain a fusion image with the prominent infrared target and clear background details,  and both with  the superior denoise function.
源自[六^维$论'文}网(加7位QQ3249`114 www.lwfree.cn

 Keywords   Fusion of Infrared and Visible Images, Joint Sparse Representation, Common and Innovation Sparse coefficients, K-SVD, OMP   
目次
1绪论1
1.1研究背景及意义.1
1.2国内外研究现状.2
1.3本文主要工作及章节安排.3
2图像融合基本原理5
2.1融合层次.5
2.2融合算法.5
2.2.1基于小波变换的图像融合算法6
2.2.2基于ICA的图像融合算法6
2.2.3基于稀疏表示的图像融合算法7
2.3融合规则7
2.3.1基于单像素点的融合规则.7
2.3.2基于区域的融合规则.8
2.4图像去噪9
2.4.1空间域去噪9
2.4.2变换域去噪.10
2.5评价体系.11
2.5.1主观评价指标11
2.5.2客观评价指标11
2.6本章小结.14
3稀疏表示理论15
3.1稀疏模型15
3.2优化算法15
3.2.1贪婪追踪算法.15
3.2.2 基于联合稀疏表示的红外与可见光图像融合研究:http://www.lwfree.cn/tongxin/20181202/26884.html
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