Selecting the optimal focus measure for autofocusing anddepth-from-focus
Subbarao, M.
Tyan, J.-K.
Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Aug 1998
Volume: 20,
Issue: 8
On page(s): 864-870
ISSN: 0162-8828
References Cited: 6
CODEN: ITPIDJ
INSPEC Accession Number: 6013889
Digital Object Identifier: 10.1109/34.709612
Current Version Published: 2002-08-06
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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