Key points are not available for this paper at this time.
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.
Subbarao et al. (Thu,) studied this question.