2026-09-15 2026, Volume 19 Issue 3

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  • RESEARCH ARTICLE
    Ye Liu, Weibing Sun, Junyu Zhang, Ying Chen, Haoqi Luo, Jintian Bian, Qing Ye, Yunlong Wu

    Wavefront coding technology employs a phase mask to modulate the phase of incident light, thereby dispersing the laser spot on the detector and achieving laser protection for optical systems. Current research has predominantly concentrated on validating laser damage at a single imaging distance, neglecting the evolution of protective capability across varying distances in the wavefront coding imaging system. To address this limitation, this study establishes a wavefront coding imaging system based on a cubic phase function and experimentally elucidates the variation of laser suppression capacity with transmission distance. Under conditions of pulsed laser-induced point damage in the visible spectrum, a strong correlation is observed between the laser suppression ratio and the laser damage threshold improvement value. Additionally, the NAFNet model is utilized to restore encoded images, resulting in high-fidelity reconstruction. The PSNR for both simulated and experimentally decoded images consistently surpasses 23 dB. Furthermore, under laser irradiation conditions, the model adeptly eliminates laser artifacts and recovers image content. This study possesses considerable practical value for the design and implementation of laser protection mechanisms in optical systems.

  • RESEARCH ARTICLE
    Dezhou Lu, Hao Chi, Liying Chen, Mengyang Jin, Jiahui Wu, Zhuning Wang, Yaoguang Ma

    In this paper, we report a mode-interference-based approach for the efficient and reliable diameter measurement of micro/nanofibers (MNFs), enabling the in situ monitoring of MNFs fabricated from both single-mode fibers (SMFs) and multimode fibers (MMFs). The proposed method integrates automated signal processing with parameter-corrected flame-brush models, establishing a real-time closed-loop feedback mechanism during the fabrication process. Within the 524–1778 nm range, measurement accuracies better than 8 nm (< 1.25%) for SMF and 5 nm (< 0.78%) for MMF are demonstrated. Furthermore, to address the challenge of reconstructing complex taper profiles, we introduce a one-dimensional convolutional neural network (1D-CNN). Trained on a physics-enhanced data set, this network enables the end-to-end precision measurement of taper morphology. Within the diameter range of 1.9–10 µm, the maximum relative error is maintained below 0.35%, with a maximum absolute error of less than 9 nm. This method demonstrates broad applicability, offering a reliable solution for the fabrication of high-performance MNF-based photonic devices.

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{"submissionFirstDecision":"30","jcrJfStr":"5.2 (2024)","editorEmail":"mamm@hep.com.cn"}

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{"submissionFirstDecision":"30","jcrJfStr":"5.2 (2024)","editorEmail":"mamm@hep.com.cn"}
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ISSN 2095-2759 (Print)
ISSN 2095-2767 (Online)
CN 10-1029/TN