SIAM Journal on Imaging Sciences, Volume 14, Issue 2, Page 441-469, January 2021.
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.

中文翻译：

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图像融合中空间权重的变分模型

SIAM影像科学杂志，第14卷，第2期，第441-469页，2021年1月。
为了在融合过程中尽可能多地保留来自输入源图像的有价值的细节，本文提出了一种基于自适应权重的图像融合总变化模型。主要思想是采用非凸能量函数，通过最大化输出图像的局部方差并保留输入图像的亮度，来同时确定输出融合图像和权重函数。为了最小化附近像素位置处的权重函数之间的差异，将权重函数的总变化正则化合并到融合过程的函数中。建立了所提出的变分模型的最小化子。此外，我们开发了一种有效的算法，可以使用原始对偶方法对模型进行数值求解，并证明了算法的收敛性。据报道，实验结果证明了该方法的有效性，其性能与其他测试方法相比具有竞争优势。