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Lightweight super-resolution multimode fiber imaging with regularized linear regression
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DOI：10.1364/OE.522201.png)
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Abstract
En 中文
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.
Keywords:
RECONSTRUCTION
SENSORS
Journal
IF:
3.3
Papers:
6.1W
Citations:
14.3W