Image fusion based on the sparse representation(SR)has become the primary research direction of the transform domain method.However,the SR-based image fusion algorithm has the characteristics of high computational com...Image fusion based on the sparse representation(SR)has become the primary research direction of the transform domain method.However,the SR-based image fusion algorithm has the characteristics of high computational complexity and neglecting the local features of an image,resulting in limited image detail retention and a high registration misalignment sensitivity.In order to overcome these shortcomings and the noise existing in the image of the fusion process,this paper proposes a new signal decomposition model,namely the multi-source image fusion algorithm of the gradient regularization convolution SR(CSR).The main innovation of this work is using the sparse optimization function to perform two-scale decomposition of the source image to obtain high-frequency components and low-frequency components.The sparse coefficient is obtained by the gradient regularization CSR model,and the sparse coefficient is taken as the maximum value to get the optimal high frequency component of the fused image.The best low frequency component is obtained by using the fusion strategy of the extreme or the average value.The final fused image is obtained by adding two optimal components.Experimental results demonstrate that this method greatly improves the ability to maintain image details and reduces image registration sensitivity.展开更多
基金the National Natural Science Foundation of China(61671383)Shaanxi Key Industry Innovation Chain Project(2018ZDCXL-G-12-2,2019ZDLGY14-02-02,2019ZDLGY14-02-03).
文摘Image fusion based on the sparse representation(SR)has become the primary research direction of the transform domain method.However,the SR-based image fusion algorithm has the characteristics of high computational complexity and neglecting the local features of an image,resulting in limited image detail retention and a high registration misalignment sensitivity.In order to overcome these shortcomings and the noise existing in the image of the fusion process,this paper proposes a new signal decomposition model,namely the multi-source image fusion algorithm of the gradient regularization convolution SR(CSR).The main innovation of this work is using the sparse optimization function to perform two-scale decomposition of the source image to obtain high-frequency components and low-frequency components.The sparse coefficient is obtained by the gradient regularization CSR model,and the sparse coefficient is taken as the maximum value to get the optimal high frequency component of the fused image.The best low frequency component is obtained by using the fusion strategy of the extreme or the average value.The final fused image is obtained by adding two optimal components.Experimental results demonstrate that this method greatly improves the ability to maintain image details and reduces image registration sensitivity.
基金The National Natural Science Foundation of China(61573183)The Open Fund of Jiangsu Key Laboratory of Big Data Analysis Technology(KXK1403)+3 种基金The Open Fund of Zhejiang Province Key Laboratory for Signal Processing(ZJKL_6_SP-OP 2014-02)The Open Fund of Guangxi Key Lab of Multi-Source Information Mining and Security(MIMS14-01)The Open Fund of Key Laboratory of Geo-Spatial Information Technology(KLGSIT2015-05)The Open Fund of MLR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral Resources(ZS1406)
文摘针对红外图像与SAR图像的灰度差异性大、两者融合图像不太符合人类视觉认知的问题,提出了一种基于联合稀疏表示的复Contourlet域红外图像与SAR图像融合方法。首先对红外图像与SAR图像分别进行复Contourlet分解。然后利用K-奇异值分解(K-Singular Value Decomposition,K-SVD)方法获得两幅源图像低频分量的过完备字典,并根据联合稀疏表示模型生成联合字典,通过正交匹配追踪(Orthogonal Matching Pursuit,OMP)方法求出源图像低频分量在联合字典下的稀疏表示系数,接着采用选择最大化策略对两个低频分量的稀疏表示系数进行选取,随后进行稀疏表示重构获得融合的低频分量;对高频分量结合视觉敏感度系数和能量匹配度两个活跃度准则进行融合,以捕获源图像丰富的细节信息。最后经复Contourlet逆变换获得融合图像。与3种经典融合方法及近年来提出的基于非下采样Contourlet变换(Non-Subsampled Contourlet Transform,NSCT)、基于稀疏表示的融合方法相比,该方法能够有效突出源图像的显著特征,最大程度地继承源图像的信息。
