Lens-free microscopy aims at recovering sample image from diffraction measurements. The acquisitions are usually processed with an inverse problem approach. Recently, deep learning has been used to further improve phase retrieval results. Here, we propose to alternate iteratively between the two algorithms, to improve the reconstruction results without losing data fidelity. We validated this method for the phase image recovery of floating cells sample at large density acquired by means of lens-free microscopy. This is a challenging case with a lot of phase wrapping artefacts that has never been successfully solved using inverse problem approaches only.

Date Published: 11 March 2020
PDF
Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124914 (11 March 2020); doi: 10.1117/12.2544812

Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)