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<p>The convergence of data-driven optics and intelligent imaging has renewed interest in multimode fibers (MMFs) as compact, high-capacity platforms for optical communication and endoscopic imaging. However, the perturbation-dependent transmission of MMFs makes the input-output mapping non-stationary, therefore, reconstruction models that are learned once must be conditioned on up-to-date physical priors to remain accurate. We present a physics-informed diffusion-based network (PID-Net) that enables robust image reconstruction through dynamically perturbed MMFs. By integrating dynamically updated, wavelength-multiplexed transmission-matrix priors, PID-Net embeds the deterministic optical transmission law into the diffusion-based framework, enabling fast recalibration and physically consistent reconstruction under dynamic perturbations. Experiments on meter-scale multimode fibers under dynamic bending demonstrate that PID-Net achieves a fourfold improvement in reconstruction quality, preserving rich structural details where conventional methods completely fail. These results demonstrate that PID-Net markedly improves robustness to fiber perturbations and provides a practical, physically grounded route toward deployable multimode-fiber imaging.</p>

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Physics-informed diffusion enables high-fidelity multimode fiber imaging under dynamic perturbations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月25日

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  • Zefeng Feng,
  • Hao Fan,
  • Hu Liu,
  • Tianyu Han,
  • Jiping Wang,
  • Yao Tan,
  • Wei Zhou,
  • Baoteng Xu,
  • Jialin Liu,
  • Jian Zheng,
  • Jiawei Sun,
  • Daxi Xiong,
  • Xibin Yang

摘要

The convergence of data-driven optics and intelligent imaging has renewed interest in multimode fibers (MMFs) as compact, high-capacity platforms for optical communication and endoscopic imaging. However, the perturbation-dependent transmission of MMFs makes the input-output mapping non-stationary, therefore, reconstruction models that are learned once must be conditioned on up-to-date physical priors to remain accurate. We present a physics-informed diffusion-based network (PID-Net) that enables robust image reconstruction through dynamically perturbed MMFs. By integrating dynamically updated, wavelength-multiplexed transmission-matrix priors, PID-Net embeds the deterministic optical transmission law into the diffusion-based framework, enabling fast recalibration and physically consistent reconstruction under dynamic perturbations. Experiments on meter-scale multimode fibers under dynamic bending demonstrate that PID-Net achieves a fourfold improvement in reconstruction quality, preserving rich structural details where conventional methods completely fail. These results demonstrate that PID-Net markedly improves robustness to fiber perturbations and provides a practical, physically grounded route toward deployable multimode-fiber imaging.