A Variational Model for Spatially Weighting in Image Fusion

In order to retain as many valuable details from the input source images as possible during the process of fusion, this paper proposes an adaptive weight based total variation model for image fusion. The main idea is to employ a nonconvex energy functional to determine simultaneously the output fused image and weight functions by maximizing the local variance of the output image and preserving the brightness of the input images. In order to minimize the differences among the weight functions at the nearby pixel locations, the total variation regularization of the weight functions is incorporated in the functional for the fusion process. The existence of minimizers to the proposed variational model is established. Furthermore, we develop an efficient algorithm to solve the model numerically by using the primal-dual method, and prove the convergence of the algorithm. Experimental results are reported to illustrate the effectiveness of the proposed method, and its performance is competitive with the other testing methods.