Nonhomogenous random fields are known to be well adapted to modeling a wide class of images. Their computational complexity generally causes their lack of appeal, we propose a more efficient model capable of capturing textures, shapes, as well as jumps typically encountered in infra-red images. The so-called Levy random fields as we show, can indeed represent a very well adapted alternative for inference applications and the like.