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The generation of tailored light with multi-core fiber (MCF) lenslessmicroendoscopes is widely used in biomedicine. However, the computer-generatedholograms (CGHs) used for such applications are typically generated byiterative algorithms, which demand high computation effort, limiting advancedapplications like in vivo optogenetic stimulation and fiber-optic cellmanipulation. The random and discrete distribution of the fiber cores inducesstrong spatial aliasing to the CGHs, hence, an approach that can rapidlygenerate tailored CGHs for MCFs is highly demanded. We demonstrate a novelphase encoder deep neural network (CoreNet), which can generate accuratetailored CGHs for MCFs at a near video-rate. Simulations show that CoreNet canspeed up the computation time by two magnitudes and increase the fidelity ofthe generated light field compared to the conventional CGH techniques. For thefirst time, real-time generated tailored CGHs are on-the-fly loaded to thephase-only SLM for dynamic light fields generation through the MCFmicroendoscope in experiments. This paves the avenue for real-time cellrotation and several further applications that require real-time high-fidelitylight delivery in biomedicine.

Lensless multicore-fiber microendoscope for real-time tailored light field generation with phase encoder neural network (CoreNet)