Super-resolution multimode fiber imaging provides the means to image samples quickly with compact and flexible setups finding many applications from biology and medicine to material science and nanolithography. Typically, fiber-based imaging systems suffer from low spatial resolution and long measurement times. State-of-the-art computational approaches can achieve fast super-resolution imaging through a multimode fiber probe but currently rely on either per-sample optimised priors or large data sets with subsequent long training and image reconstruction times. This unfortunately hinders any real-time imaging applications. Here we present an ultimately fast non-iterative algorithm for compressive image reconstruction through a multimode fiber. The proposed approach helps to avoid many constraints by determining the prior of the target distribution from a simulated set and solving the under-determined inverse matrix problem with a mathematical closed-form solution. We have demonstrated theoretical and experimental evidence for enhanced image quality and sub-diffraction spatial resolution of the multimode fiber optical system.

ASML, ARCNL, VU, UvA, RUG, NWO , National Growth Fund program NXTGEN HIGHTECH
Opt. Express
Nanoscale Imaging and Metrology

Lipp, M., Li, W., Abrashitova, K., Forré, P., & Amitonova, L. (2024). Lightweight super-resolution multimode fiber imaging with regularized linear regression. Opt. Express, 32(9), 15147–15155. doi:10.1364/oe.522201