以稀疏表示理论为基础, 研究了一种可见光和红外图像融合算法, 提出了一种稀疏系数融合规则。首先, 利用K-SVD算法对待融合图像的所有子区域进行字典学习, 得到用于稀疏向量计算的过完备字典; 然后, 计算稀疏向量, 利用正交匹配追踪算法进行求解; 最后, 提出一种基于稀疏向量最大元素绝对值的融合规则, 完成可见光和红外图像的稀疏向量融合, 得到融合图像。实验结果表明, 融合结果明显优于传统的基于 Maximum-L1-Norm融合规则的融合结果。

This paper focuses on the fusion of infrared and visible images based on the theory of sparse representation,and a fusion rule of sparse coefficients is proposed.The approach can be divided into three parts:over-complete dictionary,the algorithm of sparse vector approximation and the fusion rule.Firstly,through dictionary learning to all the patches of the visible image and infrared image to be fused by use of K-means Singular Value Decomposition (K-SVD),the over-complete dictionary for sparse vector calculation is obtained.Secondly,the sparse vector is approximated by orthogonal matching pursuit.Thirdly,the fusion rule based on absolute value of the maximum element of sparse vector is proposed,which is used for the sparse vector fusion of visible image with infrared image,and the fusion image is obtained.Experimental results show that the fusion result is obviously better than that of the method based on Maximum-L1-Norm.