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Artificial intelligence has emerged as promising tool to decode a phase imagetransmitted through a multimode fiber (MMF) by applying deep learningtechniques. By transmitting tens of thousands of images through the MMF, deepneural networks (DNNs) are capable of learning how to decipher the seeminglyrandom output speckle patterns and unveil the intrinsic input-outputrelationship. High fidelity reconstruction is obtained for datasets with alarge degree of homogeneity, which underutilizes the capacity of the combinedMMF-DNN system. Here, we show that holographic modulation can be employed toencode an additional layer of variance on the output speckle pattern, improvingthe overall transmissive capabilities of the system. Operatively we haveimplemented this by adding a holographic label to the original dataset andinjecting the resulting phase image into the fiber facet through a Fouriertransform lens. The resulting speckle pattern dataset can be clusteredprimarily by holographic label (rather than the image data), and can bereconstructed without loss of fidelity. As an application, we describe howcolour images may be segmented into RGB components and each colour componentmay then be labelled by distinct hologram. A UNET architecture was then used todecode each class of speckle patterns and reconstruct the colour image withoutthe need for temporal synchronisation between sender and receiver

Exploiting holographically encoded variance to transmit labelled images through a multimode optical fiber