Hyper-spectral imaging through a multi-mode fiber

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.