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Remote sensing image variational fusion model based on L1 norm and split Bregman

HOU Xinting;QIN Qianqing;SUN Tao;SONG Bo;FU Zhitao;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of Wuhan University;Electronic Information School of Wuhan University;  
At present,some Pan-sharpening based on variational methods are fused by minimizing the energy functional by gradient descent algorithm,but the convergence rate of the gradient descent method decreases when it near the minimum.And if the variational model contains the no differentiable of L1 norm,the gradient descent method has the problems of low robustness and complex computation.In this paper,according to the characteristics of L1 norm can keep the geometric texture of the image,split Bregman iterative has a fast convergence speed to the functional which contains L1 norm,so on the basis of the existing variational model,the L1 norm is added to the model,the energy functional cost function is constructed,and through the split Bregman iterative minimal energy functional.The fusion results on the Worldview-2 shows that the method in this paper can generate images with high spectral and high spatial resolution simultaneously.
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