Open AccessFast and accurate autofocus control using Gaussian standard deviation and gradient-based binning
Author(s) -
Peter DiMeo,
Liangling Sun,
Xian Du
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.425118
Subject(s) - autofocus , standard deviation , computer science , focus (optics) , gaussian , artificial intelligence , image processing , gaussian process , algorithm , computer vision , mathematics , optics , image (mathematics) , physics , statistics , quantum mechanics
We propose a fast and accurate autofocus algorithm using Gaussian standard deviation and gradient-based binning. Rather than iteratively searching for the optimal focus using an optimization process, the proposed algorithm directly calculates the mean of the Gaussian shaped focus measure (FM) curve to find the optimal focus location and uses the FM curve standard deviation to adapt the motion step size. The calculation only requires 3-4 defocused images to identify the center location of the FM curve. Furthermore, by assigning motion step sizes based on the FM curve standard deviation, the magnitude of the motion step is adaptively controlled according to the defocused measure, thus avoiding overshoot and unneeded image processing. Our experiment verified the proposed method is faster than the state-of-the-art Adaptive Hill-Climbing (AHC) and offers satisfactory accuracy as measured by root-mean-square error. The proposed method requires 80% fewer images for focusing compared to the AHC method. Moreover, due to this significant reduction in image processing, the proposed method reduces autofocus time to completion by 22% compared to the AHC method. Similar performance of the proposed method was observed in both well-lit and low-lighting conditions.