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Diffusion-model-based inverse problem processing for optically-measured sound field.

Hao Di, Yasuhiro Oikawa, Kenji Ishikawa

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    |November 22, 2024
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    This study introduces a novel diffusion model for optical sound-field imaging, enhancing noise reduction and sound reconstruction. The method effectively addresses challenges in high-resolution acoustic measurements, showing real-world applicability.

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    Area of Science:

    • Acoustics
    • Optical Imaging
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Optical sound-field imaging offers high spatial resolution for sound measurement.
    • Noise contamination is a significant challenge in optical sound-field imaging.
    • Inverse problems like denoising and reconstruction are crucial for accurate sound analysis.

    Purpose of the Study:

    • To propose a diffusion-model-based method for solving inverse problems in optical sound-field imaging.
    • To address noise contamination, sound-field reconstruction, and extrapolation.
    • To improve the accuracy and applicability of optical sound-field imaging.

    Main Methods:

    • A diffusion model is employed for sound-field inverse problems.
    • Range-null space decomposition is utilized as a solver to manage sound-field degradation during inference.
    • The model iteratively generates degraded sound-field information for robust processing.

    Main Results:

    • The proposed diffusion model outperforms existing deep-learning methods in denoising and reconstruction tasks.
    • Effective results were achieved in sound-field extrapolation.
    • Numerical experiments validate the method's superiority over other approaches.

    Conclusions:

    • The diffusion-model-based approach is highly effective for optical sound-field imaging inverse problems.
    • The method demonstrates significant improvements in denoising and reconstruction accuracy.
    • The model shows practical applicability for real-world acoustic measurements.