Abstract
Pixel-based auto-focusing is a long-standing topic in the literatures. It involves three main parameters: Region of Interest (ROI), image sharpness function and global maximum searching algorithm. As the mathematical description of image sharpness, sharpness function is the core issue for realizing robust auto-focusing. In this paper, the existing sharpness functions are summarized and grouped firstly, then a new space domain SUSAN (Smallest Univalue Segment Assimilating Nucleus) based sharpness function is proposed. The key problem in proposed algorithm is selecting a suitable similarity function to measure the similarities of the sampling pixel and its neighbors. In experiments, 8 similarity functions are analyzed and evaluated. Based on the evaluation results, a rough/fine two grades global maximum searching strategy is designed to realize fast and robust auto-focusing. At last, experiments verify the validity of the proposed auto-focusing algorithm.
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