  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.
