Performance evaluation of different depth from defocus (DFD) techniques

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.