Infrared and visible image fusion has been a hot issue in image fusion. In this task, a fused image containing both the gradient and detailed texture information of visible images as well as the thermal radiation and highlighting targets of infrared images is expected to be obtained. In this paper, a novel Bayesian fusion model is established for infrared and visible images. In our model, the image fusion task is cast into a regression problem. To measure the variable uncertainty, we formulate the model in a hierarchical Bayesian manner. Aiming at making the fused image satisfy human visual system, the model incorporates the total-variation(TV) penalty. Subsequently, the model is efficiently inferred by the expectation-maximization(EM) algorithm. We test our algorithm on TNO and NIR image fusion datasets with several state-of-the-art approaches. Compared with the previous methods, the novel model can generate better fused images with high-light targets and rich texture details, which can improve the reliability of the target automatic detection and recognition system.

利用贝叶斯融合模型，运用EM算法，对红外图像和可见光图像进行融合，通过加入total-variation penalty 来增强目标的自动检测和识别系统，从而得到具有更丰富细节和高光目标的融合图像。

红外和可见光图像的贝叶斯融合