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Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus
August 1998 (vol. 20 no. 8)
pp. 864-870

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—the Autofocusing Uncertainty Measure (AUM)—is useful in understanding the relation between gray-level noise and the resulting error in lens position for autofocusing. The second metric—Autofocusing Root-Mean-Square Error (ARMS error)—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.

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Index Terms:
Focus measure, focusing, autofocusing, depth-from-focus, focus analysis.
Murali Subbarao, Jenn-Kwei Tyan, "Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 864-870, Aug. 1998, doi:10.1109/34.709612
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