  Computational Cannula Microscopy is a minimally invasive imaging technique
that can enable high-resolution imaging deep inside tissue. Here, we apply
artificial neural networks to enable fast, power-efficient image
reconstructions that are more efficiently scalable to larger fields of view.
Specifically, we demonstrate widefield fluorescence microscopy of cultured
neurons and fluorescent beads with field of view of 200$\mu$m (diameter) and
resolution of less than 10$\mu$m using a cannula of diameter of only 220$\mu$m.
In addition, we show that this approach can also be extended to
macro-photography.
