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Updated: Aug 22, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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Deep learning-based image reconstruction for photonic integrated interferometric imaging.

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    This study introduces a deep learning method to enhance photonic integrated interferometric imaging (PIII). The new approach improves image quality by increasing sampling rates and reducing noise, overcoming key limitations in PIII development.

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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Artificial Intelligence

    Background:

    • Photonic integrated interferometric imaging (PIII) is an emerging technique for image formation using spatial coherence.
    • Current PIII methods face challenges with low sampling rates and noise, limiting image quality and development.
    • Deep learning offers potential solutions for enhancing image reconstruction in PIII.

    Purpose of the Study:

    • To develop a deep learning-based method for improving image quality in PIII.
    • To address the limitations of low sampling rates and noise disturbance in PIII.
    • To introduce novel network architectures and training strategies for enhanced image reconstruction.

    Main Methods:

    • A frequency-domain dataset generation method based on imaging principles was proposed.
    • Spatial-frequency dual-domain fusion networks (SFDF-Nets) were developed for image reconstruction.
    • Normalized amplitude and phase were used for network training, simplifying complex data handling.
    • Multi-frame data from rotation sampling was fused to increase the sampling rate.
    • An inverse fast Fourier transform (IFFT) loss was introduced for frequency-domain network training.

    Main Results:

    • The SFDF-Nets effectively fused multi-frame data, increasing the sampling rate.
    • Dual-domain supervised learning and frequency domain fusion generated high-quality spatial images.
    • The proposed method demonstrated significant improvements in image quality metrics.
    • Peak Signal-to-Noise Ratio (PSNR) improved by 5.64 dB.
    • Structural Similarity Index Measure (SSIM) improved by 0.20.

    Conclusions:

    • The deep learning-based method significantly enhances reconstructed image quality in PIII.
    • The SFDF-Nets architecture and IFFT loss provide an effective approach for PIII reconstruction.
    • This work overcomes key limitations in PIII, paving the way for new advancements in interferometric imaging.