Monocular depth prediction plays a crucial role in 3D scenes understanding and perception.At present,deep learning methods have achieved great performance,however,their performance is dependent on training data.In complex scenes,the assumption of global light invariance based on depth cues is not effective.To this end,we propose a depth prediction method based on cross Bayesian fusion of global and local atmospheric light.Specifically,we use the global and the designed local atmospheric light scattering model to predict the initial depth map respectively.And then we construct a cross Bayesian model to fuse depth maps,in order to better realize the complementarity between the two models.Finally,we optimize the depth map by using the edge filtering mechanism.A series of experiments can prove that the model designed can achieve good results in the depth prediction datasets,and help to improve the accuracy of salient object detection task.