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Fast and Accurate Autofocus Control Using Guassian Standard Deviation and Gradient-Based Binning

Abstract
We propose a fast and accurate autofbcus 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 autofbcus 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. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Type
article
article
Date
2021-01-01
Publisher
Degree
Advisors
Rights
UMass Amherst Open Access Policy
License
http://creativecommons.org/licenses/by/4.0/