Fast autofocusing based on single-pixel moment detection

Traditional image processing-based autofocusing techniques require the acquisition, storage, and processing of large amounts of image sequences, constraining focusing speed and cost. Here we propose an autofocusing technique, which directly and exactly acquires the geometric moments of the target object in real time at different locations by means of a proper image modulation and detection by a single-pixel detector. An autofocusing criterion is then formulated using the central moments, and the fast acquisition of the focal point is achieved by searching for the position that minimizes the criterion. Theoretical analysis and experimental validation of the method are performed and the results show that the method can achieve fast and accurate autofocusing. The proposed method requires only three single-pixel detections for each focusing position of the target object to evaluate the focusing criterion without imaging the target object. The method does not require any active object-to-camera distance measurement. Comparing to local differential methods such as contrast or gradient measurement, our method is more stable to noise and requires very little data compared with the traditional image processing methods. It may find a wide range of potential applications and prospects, particularly in low-light imaging and near-infra imaging, where the level of noise is typically high. Dongfeng Shi and colleagues design an autofocusing algorithm which required fewer sampling pixels. Their method performs well in low light high noise imaging.