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Related Experiment Video

Updated: Aug 25, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Quantized spiral-phase-modulation based deep learning for real-time defocusing distance prediction.

Zezheng Zhang, Ryan K Y Chan, Kenneth K Y Wong

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    This summary is machine-generated.

    Whole slide imaging (WSI) requires precise sample positioning. This study introduces a novel autofocusing method using quantized spiral phase modulation and a lightweight neural network, significantly improving accuracy and speed for pathology diagnostics.

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    Area of Science:

    • Digital pathology
    • Computational imaging
    • Optical microscopy

    Background:

    • Whole slide imaging (WSI) is crucial for remote and collaborative pathological diagnosis.
    • Achieving optimal sample positioning and artifact-free imaging in WSI remains a challenge.
    • Traditional autofocusing methods for WSI are often not universal or are time-consuming.

    Purpose of the Study:

    • To develop a more efficient and accurate autofocusing method for whole slide imaging.
    • To address the challenge of sample positioning and defocusing artifacts in WSI.
    • To enable real-time autofocusing on edge devices with limited resources.

    Main Methods:

    • Application of quantized spiral phase modulation in the Fourier domain of captured images.
    • Utilizing a lightweight neural network for predicting defocusing distance.
    • Evaluation on an open dataset to assess prediction error and speed.

    Main Results:

    • Significant reduction in average prediction error compared to previous methods.
    • Achieved prediction error lower than any reported on the open dataset.
    • Demonstrated high prediction speed suitable for real-time applications.

    Conclusions:

    • The proposed method offers a highly accurate and fast autofocusing solution for WSI.
    • Quantized spiral phase modulation combined with a lightweight neural network overcomes limitations of traditional methods.
    • The approach is suitable for deployment on edge devices, enhancing the efficiency of digital pathology workflows.