7 November 2005 Performance evaluation of different depth from defocus (DFD) techniques
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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.
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Tao Xian, Murali Subbarao, "Performance evaluation of different depth from defocus (DFD) techniques", Proc. SPIE 6000, Two- and Three-Dimensional Methods for Inspection and Metrology III, 600009 (7 November 2005); doi: 10.1117/12.629611; https://doi.org/10.1117/12.629611
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