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

Autofocusing (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 self-adaptability, 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.