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arXiv 2302.07568physics.opticseess.IV
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多模光纤超光谱成像

Hyper-spectral imaging through a multi-mode fiber

Alim Yolalmaz, Emre Yüce

摘要:

多模光纤在提供大量传输模式的情况下提供增加量的数据传输速率。不幸的是,多模光纤中增加的模式数量由于这些模式的干涉而阻碍了信息的准确传递,这导致了随机散斑图案。系统的复杂性阻碍了系统的解析表达,从而导致信息的丢失。然而,深度学习算法可以用来有效地恢复信息。在这项研究中,我们利用深度学习架构从电信波长(C波段)的输出散斑图案重建输入彩色图像。我们的模型成功地识别了26个不同波长和26个不同字母的高光谱散斑图案。值得注意的是,我们可以重建完整的输入图像,只有通过分析输出散斑图案的一小部分。因此,我们设法在不牺牲分类精度的情况下减少计算量。我们相信,这项研究将在许多领域显示出变革性的影响:生物医学成像,通信,传感和光子计算。

英文摘要:

Multi-mode fibers provide an increased amount of data transfer rates given a large number of transmission modes. Unfortunately, the increased number of modes in a multi-mode fiber hinders the accurate transfer of information due to interference of these modes which results in a random speckle pattern. The complexity of the system impedes the analytical expression of the system thereby the information is lost. However, deep learning algorithms can be used to recover the information efficiently. In this study, we utilize deep learning architecture to reconstruct input colored images from the output speckle patterns at telecommunication wavelength (C-band). Our model successfully identifies hyper-spectral speckle patterns at twenty-six separate wavelengths and twenty-six distinct letters. Remarkably, we can reconstruct the complete input images only by analyzing a small portion of the output speckle pattern. Thereby, we manage to decrease the computational load without sacrificing the accuracy of the classification. We believe that this study will show a transformative impact in many fields: biomedical imaging, communication, sensing, and photonic computing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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