Research of autofocus algorithms for high-content microscopy imaging based on the prediction-verification peak strategy
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1.School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; 2.School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China

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TH742 TP391.7

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    Abstract:

    To address the stringent autofocus requirements of high speed and accuracy for the high-content microscopy imaging systems, this study proposes a peak search strategy based on the active sampling and iterative weighted curve fitting, which aims to achieve the stable and precise focus positioning with the fewest sampling steps. The method begins by establishing a quadratic curve model using four initial sampling points to predict the focus region, thereby constructing a closed-loop mechanism of "prediction-verification-optimization." In each iteration, the system actively selects the sampling position with the highest information gain based on the current fitted model, while dynamically reducing the impact of outlier noise on the fitting results through the weighted least square criterion. To further enhance the reliability of search, an uncertainty assessment mechanism is introduced as the criterion for iteration termination, which quantifies the credibility of model predictions by analyzing the distribution of fitting residuals and the clustering of sampling points. Simultaneously, a management strategy of fixed-scale intelligent point set is employed by consistently maintaining the four most representative sampling points for modeling, which ensures that the model remains focused on the vicinity of optimal region. This approach improves the computational efficiency while enhancing the local characterization capabilities. Experimental results show that the method achieves the stable convergence under different initial position conditions, requiring an average of only 6 to 8 sampling steps to achieve the focusing accuracy within ±4 μm. Compared to the fastest sampling time of 5.75 seconds for traditional methods, the proposed method completes focusing as fast as 2.75 seconds and provides the efficiency improvement of over 50%. Moreover, it maintains the excellent robustness and adaptability in the complex imaging environments with high noises and nonlinear interference. This study provides a reliable technical solution to achieve the rapid and precise autofocus in the high-throughput microscopy imaging.

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  • Received:
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  • Online: April 08,2026
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