Reconstructing 3D depth information from 2D defocus images is one of the top important research topics in computer vision. However

existing methods need to change the camera parameters

such as the focal length of the lens

the distance of the focused image from the lens plane and the radius of the lens

to attain the defocus images of different blurring degree. Unfortunately

in some cases with high level of magnification cameras

any change of any parameter will destroy the cameras drastically

so the application field of many existent algorithms is strictly restricted. Therefore

in this paper

a novel Depth from Defocus (DFD) method is proposed to solve this problem. First

two different blurred images are captured through changing depth. Second

the relation between depth and blurring is discussed based on the blurred imaging model obtained from the concept of relative blurring and the diffusion equation. Finally

the depth reconstruction is completed by solving an optimization problem. This proposed algorithm which does not need change any camera parameters or compute the focus image is easy to be realized. What’s more

the results of simulations and error analysis show that this method can reconstruct depth information with high precision and can be used in micro/nano manipulation and fast detection which are sensitive to camera parameters.