基于判别字典学习与形态成分分解的多源图像融合
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摘要
提出了一种基于形态成分分析的多源图像融合方法。为了将源图像中不同形态结构的卡通-纹理成分分离,把图像的分解问题转化为图像的分类问题,设计了卡通纹理判别字典学习模型。考虑到图像分解不仅与字典有关,还与分解的策略有关,设计了一种新的图像分解模型。在模型中,将纹理成分看成叠加在源图像卡通成分上的噪声,引入非局部均值相似性的一致性正则项,来约束稀疏编码系数的解空间。根据对应成分的编码系数l1范数值最大来选取融合图像的编码系数。实验结果表明,无论是在视觉效果还是在客观指标上,方法都具有更好的融合性能。
A multi-source image fusion method based on morphological component analysis is proposed.In order to separate the cartoon texture components of different morphological structures in source images,the image decomposition problem is transformed into the image classification problem and a cartoon-texture discriminant dictionary learning model is designed.Consiering that image decomposition is not only related to dictionary but also to decomposition strategy,a new image decomposition model is designed.In this model,the texture component is considered as noise superimposed on the cartoon component of source image,and introduces the consistency regular term of nonlocal mean similarity to constrain the solution space of sparse coding coefficients.According to the maximum values of corresponding component coding coefficients l1 to select the coding coefficients of fusion images.The experiments have verified that the proposed method has better fusion performance in both subjective visual quality and objective indicator.
A multi-source image fusion method based on morphological component analysis is proposed.In order to separate the cartoon texture components of different morphological structures in source images,the image decomposition problem is transformed into the image classification problem and a cartoon-texture discriminant dictionary learning model is designed.Consiering that image decomposition is not only related to dictionary but also to decomposition strategy,a new image decomposition model is designed.In this model,the texture component is considered as noise superimposed on the cartoon component of source image,and introduces the consistency regular term of nonlocal mean similarity to constrain the solution space of sparse coding coefficients.According to the maximum values of corresponding component coding coefficients l1 to select the coding coefficients of fusion images.The experiments have verified that the proposed method has better fusion performance in both subjective visual quality and objective indicator.
引文
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[7]Cunha A L D,Zhou J P,Do M N.The nonsubsampled contourlet transform:theory,design,and applications[J].IEEE Transactions on Image Processing,2006,15(10):3089—3101.
[8]Li H F,Qiu H M,Yu Z T,et al.Infrared and visible image fusion scheme based on NSCT and low-level visual features[J].Infrared Physics&Technology,2016,76:174—184.
[9]Li X S,Li H F,Yu Z T,et al.Multifocus image fusion scheme based on the multiscale curvature in nonsubsampled contourlet transform domain[J].Optical Engineering,2015,54(7):073115.
[10]Aharon M,Elad M,Bruckstein A.K-svd:an algorithm for designing overcomplete dictionaries for spare representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311—4322.
[11]Kim M,Han D K,Ko H.Joint patch clustering-based dictionary learning for multimodal image fusion[J].Information Fusion,2016,27:198—214.
[12]Jiang Y,Wang M H.Image fusion with morphological component analysis[J].Information Fusion,2014,18(1):107—118.
[13]Liu S P,Xian Y T,Li H F,et al.Text detection in natural scene images using morphological component analysis and laplacian dictionary[J].IEEE/CAA Journal of Automatica Sinica,2017,99:1—9.
[14]Dong W S,Zhang L,Shi M G,et al.Nonlocally centralized sparse representation for image restoration[J].IEEE Transactions on Image Processing,2013,22(4):1620—1630.
[15]Daubechies I,Defrese M,Demol C.An iterative thresholding algorithm for linear inverse problems with sparsity constraint[J].Communications on Pure and Applied Mathematics,2004,57(11):1413—1457.
[16]Wu F,Jing X,You X,et al.Multi-view low-rank dictionary learning for image classification[J].Pattern Recognition,2016,50:143—154.
[17]Liu Y,Liu S P,Wang Z F.A general framework for image fusion based on multi-scale transform and sparse representation[J].Information Fusion,2015,24:147—164.
[18]Liu Y,Wang Z F.Simultaneous image fusion and denoising with adaptive sparse representation[J].IET Image Processing,2015,9(5):347—357.
[19]Qu G,Zhang D,Yan P.Information measure for performance of image fusion[J].Electron Letter,2002,38(7):313—315.
[20]Liu Z,Blasch E,Xue Z,et al.Objective assessment of multiresolution image fusion algorithms for ontext enhancement in night vision:a comparative study[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(1):94—109.
[21]Zhao J,Laganiere R,Liu Z.Performance assessment of combinative pixellevel image fusion based on an absolute feature measurement[J].International Journal of Innovative Computing Information and Control,2007,3(6):1433—1447.