Anisotropic Diffusion for Depth Estimation in Shape from Focus Systems

Abstract

Shape from focus is a monocular method that uses the camera’s focus as the primary indicator for depth estimation. The initial depth map is usually improved by penalizing the L2 regularizer as a smoothness constraint, which tends to smoothen the structural details due to linear diffusion. In this article, we propose an energy minimization-based framework to improve the initial depth map by utilizing a nonlinear, spatial technique, called anisotropic diffusion as a smoothness constraint, which is combined with a fidelity term that incorporates the focus values of the initial depth to enhance structural aspects of the observed scene. Experiments are conducted on synthetic and real datasets which demonstrate that the proposed method can significantly improve the depth maps.