In this paper, we propose an infrared and visible image fusion framework based on the consensus problem. Most current infrared and visible image fusion models aim to transfer only one characteristic of each source domain to the final fusion result. This mechanism limits the performances of fusion algorithms under different conditions. We present a general fusion framework based to solve the global variable consensus optimization problem through altering direction method of multipliers (ADMM). We identified that combination of the local operators allows smooth transfer of superficial characteristics of the source domain into the fusion result. Our modification of ADMM enables us to expand the fusion algorithm's compatibility by tackling various setting including dimensionality, data types and style. The qualitative and quantitative experiment results demonstrate that, compared with other state-of-the-art algorithms, the proposed method can provide competitive performance in transferring features, structures, and information from source images to fusion results.

在本文中，我们提出了基于共识问题的红外和可见图像融合框架。当前大多数红外和可见图像融合模型旨在将每个源域的一个特征仅转移到最终融合结果。这种机制限制了融合算法在不同条件下的性能。我们提出了一个通用的融合框架，通过改变乘数的方向方法（ADMM）解决全局变量共识优化问题。我们发现，本地运算符的组合允许将源域的表面特征平稳地转移到融合结果中。我们对ADMM的修改使我们能够通过处理各种设置（包括维度，数据类型和样式）来扩展融合算法的兼容性。