Open AccessDeep-ROCS: from speckle patterns to superior-resolved images by deep learning in rotating coherent scattering microscopy
Author(s) -
Alon Saguy,
Felix Jünger,
Aviv Peleg,
Boris Ferdman,
Elias Nehme,
Alexander Rohrbach,
Yoav Shechtman
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.424730
Subject(s) - optics , speckle pattern , microscopy , scattering , spatial frequency , image resolution , diffraction , speckle imaging , physics , materials science
Rotating coherent scattering (ROCS) microscopy is a label-free imaging technique that overcomes the optical diffraction limit by adding up the scattered laser light from a sample obliquely illuminated from different angles. Although ROCS imaging achieves 150 nm spatial and 10 ms temporal resolution, simply summing different speckle patterns may cause loss of sample information. In this paper we present Deep-ROCS, a neural network-based technique that generates a superior-resolved image by efficient numerical combination of a set of differently illuminated images. We show that Deep-ROCS can reconstruct super-resolved images more accurately than conventional ROCS microscopy, retrieving high-frequency information from a small number (6) of speckle images. We demonstrate the performance of Deep-ROCS experimentally on 200 nm beads and by computer simulations, where we show its potential for even more complex structures such as a filament network.