VARIATIONAL IMAGE FUSION WITH FIRST AND SECOND-ORDER GRADIENT INFORMATION  

作　　者：Fang Li Tieyong Zeng 

机构地区：[1]Department of Mathematics, East China Normal University, and Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice, Shanghai, China [2]Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

出　　处：《Journal of Computational Mathematics》2016年第2期200-222,共23页计算数学（英文）

基　　金：Acknowledgments. This work is supported by the 973 Program （2011CB707104）, the Science and Technology Commission of Shanghai Municipality （STCSM） 13dz2260400, the National Science Foundation of China （Nos. 11001082, 11271049）, and RGC 211710, 211911, 12302714 and RFGs of HKBU.

摘　　要：Image fusion is important in computer vision where the main goal is to integrate several sources images of the same scene into a more informative image. In this paper, we propose a variational image fusion method based on the first and second-order gradient information. Firstly, we select the target first-order and second-order gradient information from the source images by a new and simple salience criterion. Then we build our model by requiring that the first-order and second-order gradient information of the fused image match with the target gradient information, and meanwhile the fused image is close to the source images. Theoretically, we can prove that our variational model has a unique minimizer. In the numerical implementation, we take use of the split Bregman method to get an efficient algorithm. Moreover, four-direction difference scheme is proposed to discrete gradient operator, which can dramatically enhance the fusion quality. A number of experiments and comparisons with some popular existing methods demonstrate that the proposed model is promising in various image fusion applications.Image fusion is important in computer vision where the main goal is to integrate several sources images of the same scene into a more informative image. In this paper, we propose a variational image fusion method based on the first and second-order gradient information. Firstly, we select the target first-order and second-order gradient information from the source images by a new and simple salience criterion. Then we build our model by requiring that the first-order and second-order gradient information of the fused image match with the target gradient information, and meanwhile the fused image is close to the source images. Theoretically, we can prove that our variational model has a unique minimizer. In the numerical implementation, we take use of the split Bregman method to get an efficient algorithm. Moreover, four-direction difference scheme is proposed to discrete gradient operator, which can dramatically enhance the fusion quality. A number of experiments and comparisons with some popular existing methods demonstrate that the proposed model is promising in various image fusion applications.

关 键 词：Image fusion Feature selection Bounded variation Second bounded variation Split Bregman 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN911.73[自动化与计算机技术—计算机科学与技术]