Depth Estimation Method for Monocular Camera Defocus Images in Microscopic Scenes

When using a monocular camera for detection or observation, one only obtain two-dimensional information, which is far from adequate for surgical robot manipulation and workpiece detection. Therefore, at this scale, obtaining three-dimensional information of the observed object, especially the depth information estimation of the surface points of each object, has become a key issue. This paper proposes two methods to solve the problem of depth estimation of defiant images in microscopic scenes. These are the depth estimation method of the defocused image based on a Markov random field, and the method based on geometric constraints. According to the real aperture imaging principle, the geometric constraints on the relative defocus parameters of the point spread function are derived, which improves the traditional iterative method and improves the algorithm’s efficiency.