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

IF 3.2 2区 物理与天体物理 Q2 OPTICS Optics express Pub Date : 2024-11-04 DOI:10.1364/OE.537802
Hao Di, Yasuhiro Oikawa, Kenji Ishikawa
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引用次数: 0

Abstract

This paper proposes a diffusion-model-based method for addressing inverse problems in optical sound-field imaging. Optical sound-field imaging, known for its high spatial resolution, measures sound by detecting small variations in the refractive index of air caused by sound but often suffers from unavoidable noise contamination. Therefore, we present a diffusion model-based approach for sound-field inverse problems, including denoising, noisy sound-field reconstruction and extrapolation. During inference, sound-field degradation is introduced into the inverse denoising process, with range-null space decomposition used as a solver to handle degradation, iteratively generating degraded sound-field information. Numerical experiments show that our method outperforms other deep-learning-based methods in denoising and reconstruction tasks, and obtains effective results in extrapolation task. The experimental results demonstrate the applicability of our model to the real world.

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基于扩散模型的光学声场测量逆问题处理。
本文提出了一种基于扩散模型的方法,用于解决光学声场成像中的逆问题。光学声场成像以其空间分辨率高而著称,它通过检测声音引起的空气折射率的微小变化来测量声音,但往往受到不可避免的噪声污染的影响。因此,我们提出了一种基于扩散模型的声场反问题方法,包括去噪、噪声声场重建和外推。在推理过程中,声场劣化被引入逆去噪过程,范围-空空间分解被用作处理劣化的求解器,迭代生成劣化的声场信息。数值实验表明,在去噪和重建任务中,我们的方法优于其他基于深度学习的方法,并在外推法任务中取得了有效的结果。实验结果证明了我们的模型在现实世界中的适用性。
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来源期刊
Optics express
Optics express 物理-光学
CiteScore
6.60
自引率
15.80%
发文量
5182
审稿时长
2.1 months
期刊介绍: Optics Express is the all-electronic, open access journal for optics providing rapid publication for peer-reviewed articles that emphasize scientific and technology innovations in all aspects of optics and photonics.
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