This paper aims to investigate a novel shape from focus (SFF) algorithm based on maximum correntropy Kalman filtering (SFF-MCKF) for solving the problem that traditional SFF methods are weak in de-noising and spatial continuity.
To be specific, it was first assumed that the predicted depth of next pixel is equal to the depth of the current pixel according to spatial continuity. Besides, the observing data are derived from the estimation of traditional SFF and the corresponding covariance of noise is adaptively calculated by the entropy along the optical axis. Finally, to enhance robustness, we systematically conduct MCKF iteratively in four transfer directions that are 0°, 90°, 45° and −45°, respectively.
The experimental results indicate that the robustness of SFF-MCKF facing noises as well as the spatial continuity is better than that of the existing representative ones.
As the proposed method is aimed at precision objects, high demand is for experimental device. Unstable device unregister the source images, which is unfavorable for observing data.
SFF-MCKF can be applied to many precision object measurements without significant drifts, such as the surface reconstruction of metal objects and electronic components. Besides, the computation cost is low, and SFF-MCKF has a wide range of real-time industrial applications.
