一种基于变分的红外和微光遥感图像融合模型

为了充分利用红外和微光遥感图像中的互补信息，使其便于目视解译，提出了一种基于变分的图像融合方法。该变分模型定义了细节注入项和结构保真项，在保持红外和微光图像光谱特性的同时，还改进了融合图像的空间细节和结构特性；引入了正则化能量项，保证了泛函最优解的平滑性。基于梯度下降流，通过数值迭代获得了融合图像。实验结果表明，该模型能够获取兼具丰富细节信息和光谱信息的融合图像。与Laplacian金字塔分解方法和多孔小波方法相比，本文方法具有更佳的融合性能。

A Fusion Model of Infrared and Low-light Remote Sensing Images Based on Variation

To make full use of the complementary information in infrared and low-light remote sensing images and make it more convenient for visual interpretation, an image fusion method based on variation is proposed. In this variation-based mode, both the detail injection term and the structure fidelity term are defined. The spatial detail and structure characteristics of the fused images are also improved while the spectral characteristics of the infrared and low-light images are kept. A regularity energy term is incorporated into the fusion model so as to ensure the smoothness of the solution. On the basis of gradient descent flow, the fused images are obtained by numerical iteration. The experimental results show that the model can obtain the fused images containing abundant spatial and spectral information. Compared with the Laplacian pyramid decomposition-based and undecimated wavelet transform-based methods, the proposed model exhibits better fusion performance.