Global depth from defocus with fixed camera parameters

Reconstruction depth from 2D images is an important research issue in computer vision, and depth from defocus (DFD) is an effective way which takes the blurred degree of the region images whose depth of field is limit as the tool of computing depth. Now though there are many DFD methods, they all need to change camera parameters in order to attain blurred images, such as the focal length of the lens, the radius of the lens. If cameras with high level of amplification are used, it is inhibitory to change camera parameters. Therefore, in this paper a novel DFD method is proposed. First, two different blurred images are captured through changing depth. Second, the blurred imaging model is constructed with the relative blurring and the diffusion equation, and the relation between depth and blurring is discussed from two aspects. Finally, the problem of computing depth is transformed into an optimization issue. The method proposed in this paper does not need to change camera parameters, so the process is very simple and can be used in some special applications. The simulation results show that this method can attain depth with high precision.