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

arXiv: https://arxiv.org/abs/2111.12758 · HTML: https://mlsanity.com/paper/2111.12758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 57 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://mlsanity.com/paper/1705.04286/) (2017)

## Abstract (arXiv)

The generation of tailored light with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algorithms, which demand high computation effort, limiting advanced applications like in vivo optogenetic stimulation and fiber-optic cell manipulation. The random and discrete distribution of the fiber cores induces strong spatial aliasing to the CGHs, hence, an approach that can rapidly generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel phase encoder deep neural network (CoreNet), which can generate accurate tailored CGHs for MCFs at a near video-rate. Simulations show that CoreNet can speed up the computation time by two magnitudes and increase the fidelity of the generated light field compared to the conventional CGH techniques. For the first time, real-time generated tailored CGHs are on-the-fly loaded to the phase-only SLM for dynamic light fields generation through the MCF microendoscope in experiments. This paves the avenue for real-time cell rotation and several further applications that require real-time high-fidelity light delivery in biomedicine.