Selecting the optimal focus measure for autofocusing and depth-from-focus

被引:262
作者
Subbarao, M [1 ]
Tyan, JK [1 ]
机构
[1] SUNY Stony Brook, Dept Elect Engn, Stony Brook, NY 11794 USA
关键词
focus measure; focusing; autofocusing; depth-from-focus; focus analysis;
D O I
10.1109/34.709612
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 tb 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.
引用
收藏
页码:864 / 870
页数:7
相关论文
共 6 条
[1]  
KROTKOV E, 1987, INT J COMPUT VISION, V1, P223, DOI 10.1007/BF00127822
[2]  
Nayar S. K., 1992, Proceedings. 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.92CH3168-2), P302, DOI 10.1109/CVPR.1992.223259
[3]   FOCUSING TECHNIQUES [J].
SUBBARAO, M ;
CHOI, T ;
NIKZAD, A .
OPTICAL ENGINEERING, 1993, 32 (11) :2824-2836
[4]  
SUBBARAO M, 1995, P SOC PHOTO-OPT INS, V2598, P89, DOI 10.1117/12.220891
[5]  
SUBBARAO M, 1996, P SPIE C, V2909, P162
[6]  
TYAN JK, 1997, THESIS SUNY STONY BR