Multimode fiber-based greyscale image projector enabled by neural networks with high generalization ability.

Multimode fibers (MMFs) are emerging as promising transmission media for delivering images. However, strong mode coupling inherent in MMFs induces difficulties in directly projecting two-dimensional images through MMFs. By training two subnetworks named Actor-net and Model-net synergetically, [Nature Machine Intelligence2, 403 (2020)10.1038/s42256-020-0199-9] alleviated this issue and demonstrated projecting images through MMFs with high fidelity. In this work, we make a step further by improving the generalization ability to greyscale images. The modified projector network contains three subnetworks, namely forward-net, backward-net, and holography-net, accounting for forward propagation, backward propagation, and the phase-retrieval process. As a proof of concept, we experimentally trained the projector network using randomly generated phase maps and their corresponding resultant speckle images output from a 1-meter-long MMF. With the network being trained, we successfully demonstrated projecting binary images from MNIST and EMNIST and greyscale images from Fashion-MNIST, exhibiting averaged Pearson's correlation coefficients of 0.91, 0.92, and 0.87, respectively. Since all these projected images have never been seen by the projector network before, a strong generalization ability in projecting greyscale images is confirmed.