Learning to transmit images through optical speckle of a multimode fiber with high fidelity

Multimode fibers provide a unique opportunity for exploring the spatial degrees of freedom for high throughput light transmission. However, the modal dispersion prevents from the straightforward application of multimode fibers for space division multiplexing, such as image transmission. Herein, we propose and experimentally demonstrate a deep neural network termed multimode fiber inverse-scattering net for overcoming the modal dispersion induced scrambling in multimode fibers. Such a network is capable of transmitting grayscale image through the multimode fiber with high fidelity. 256-level grayscale images with 128 x 128 spatial channels encoded in the input wavefront can be retrieved from the output optical speckle patterns, where the average Pearson correlation coefficient and structural similarity index are as large as 0.97 and 0.95, respectively. Our results demonstrate that the proposed deep neural network has an excellent ability for learning the relationship between the input and output optical fields of a multimode fiber, which might facilitate the realization of high throughput space division multiplexing through multimode fibers. Published under an exclusive license by AIP Publishing.

Automated summary

The multimode fiber inverse-scattering net overcomes the modal dispersion in multimode fibers, enabling high fidelity image transmission.