Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus

Authors:
Murali Subbarao;Jenn-Kwei Tyan
Affiliations:
State Univ. of New York, Stony Brook;State Univ. of New York, Stony Brook
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1998

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Abstract

A method is described for selecting the optimal focus measure with respect to gray-level noise from a given set of focus measures in passive autofocusing and depth-from-focus applications. The method is based on two new metrics that have been defined for estimating the noise-sensitivity of different focus measures. The first metric驴the Autofocusing Uncertainty Measure (AUM)驴is useful in understanding the relation between gray-level noise and the resulting error in lens position for autofocusing. The second metric驴Autofocusing Root-Mean-Square Error (ARMS error)驴is an improved metric closely related to AUM. AUM and ARMS error metrics are based on a theoretical noise sensitivity analysis of focus measures, and they are related by a monotonic expression. The theoretical results are validated by actual and simulation experiments. For a given camera, the optimally accurate focus measure may change from one object to the other depending on their focused images. Therefore, selecting the optimal focus measure from a given set involves computing all focus measures in the set.