A texture–analysis–based design method for self‐adaptive focus criterion function

Abstract

SummaryAutofocusing (AF) criterion functions are critical to the performance of a passive autofocusing system in automatic video microscopy. Most of the autofocusing criterion functions proposed are dependent on the imaging system and image captured by the objective being focused or ranged. This dependence destabilizes the performance of the system when the criterion functions are applied to objectives with different characteristics. In this paper, a new design method for autofocusing criterion functions is introduced. This method enables the system to have the ability to tell the texture directional information of the objective. Based on this information, the optimal focus criterion function specific to one texture direction is designed, voiding blindly using autofocusing functions which cannot perform well when applied to the certain surface and can even lead to failure of the whole process. In this way, we improved the selfadaptability, robustness, reliability and focusing accuracy of the algorithm. First, the grey-level co-occurrence matrices of real-time images are calculated in four directions. Next, the contrast values of the four matrices are computed and then compared. The result reflects the directional information of the measured objective surfaces. Finally, with the directional information, an adaptive criterion function is constructed. To demonstrate the effectiveness of the new focus algorithm, we conducted experiments on different texture surfaces and compared the results with those obtained by existing algorithms. The proposed algorithm excellently performs with different measured objectives.

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“…The threshold T is defined as: where is the mean and is the minimum value of the four contrast values of . A texture direction vector was defined (Liang & Qu, ) as D = [D1, D2, D3, D4], where D1, D2, D3, and D4 correspond to the texture directions of 0°, 45°, 90°, and 135°, respectively, and are initialized to 0. If any value of was smaller than T , then the corresponding value of D was set to 1.…”

“…Different focus criterion functions perform quite differently even for the same sample as shown in Figure . The figure presents an evaluation of the image quality obtained with six focus criterion functions: the normal variance (Groen, Young, & Ligthart, ; Sun, Duthaler, & Nelson, ), auto‐correlation (Sun et al, ; Vollath, ), entropy (Firestone, Cook, Culp, Talsania, & Preston, ), Laplacian (Wang, Yuanda, Guangjie, & Shijie, ), Tenenbaum gradient (or Tenegrad) (Yeo, Ong, & Sinniah, ), and self‐adaptive (Liang & Qu, ) functions. The function values are normalized and the correct focal position is at 0.293 mm.…”