Computational imaging through a fiber-optic bundle

Compressive sensing (CS) has proven to be a viable method for reconstructing high-resolution signals using low resolution measurements. Integrating CS principles into an optical system allows for higher-resolution imaging using lower-resolution sensor arrays. In contrast to prior works on CS-based imaging, our focus in this paper is on imaging through fiber-optic bundles, in which manufacturing constraints limit individual fiber spacing to around 2 pm. This limitation essentially renders fiber-optic bundles as low-resolution sensors with relatively few resolvable points per unit area. These fiber bundles are often used in minimally invasive medical instruments for viewing tissue at macro and microscopic levels. While the compact nature and flexibility of fiber bundles allow for excellent tissue access in-vivo, imaging through fiber bundles does not provide the fine details of tissue features that is demanded in some medical situations. Our hypothesis is that adapting existing CS principles to fiber bundle-based optical systems will overcome the resolution limitation inherent in fiber-bundle imaging. In a previous paper we examined the practical challenges involved in implementing a highly parallel version of the single-pixel camera while focusing on synthetic objects. This paper extends the same architecture for fiber-bundle imaging under incoherent illumination and addresses some practical issues associated with imaging physical objects. Additionally, we model the optical non-idealities in the system to get lower modelling errors.