  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.
