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Proceedings Paper

Performance evaluation of different depth from defocus (DFD) techniques
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Paper Abstract

In this paper, several binary mask based Depth From Defocus (DFD) algorithms are proposed to improve autofocusing performance and robustness. A binary mask is defined by thresholding image Laplacian to remove unreliable points with low Signal-to-Noise Ratio (SNR). Three different DFD schemes-- with/without spatial integration and with/without squaring-- are investigated and evaluated, both through simulation and actual experiments. The actual experiments use a large variety of objects including very low contrast Ogata test charts. Experimental results show that autofocusing RMS step error is less than 2.6 lens steps, which corresponds to 1.73%. Although our discussion in this paper is mainly focused on a spatial domain method STM1, this technique should be of general value for different approaches such as STM2 and other spatial domain based algorithms.

Paper Details

Date Published: 7 November 2005
PDF: 13 pages
Proc. SPIE 6000, Two- and Three-Dimensional Methods for Inspection and Metrology III, 600009 (7 November 2005); doi: 10.1117/12.629611
Show Author Affiliations
Tao Xian, State Univ. of New York at Stony Brook (United States)
Murali Subbarao, State Univ. of New York at Stony Brook (United States)


Published in SPIE Proceedings Vol. 6000:
Two- and Three-Dimensional Methods for Inspection and Metrology III
Kevin G. Harding, Editor(s)

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