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Out‐of‐focus background subtraction for fast structured illumination super‐resolution microscopy of optically thick samples
Author(s) -
VERMEULEN P.,
ZHAN H.,
ORIEUX F.,
OLIVOMARIN J.C.,
LENKEI Z.,
LORIETTE V.,
FRAGOLA A.
Publication year - 2015
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12259
Subject(s) - focus (optics) , resolution (logic) , sample (material) , optics , depth of focus (tectonics) , microscopy , image resolution , background subtraction , artificial intelligence , computer science , subtraction , optical sectioning , computer vision , materials science , physics , mathematics , pixel , geology , paleontology , tectonics , subduction , arithmetic , thermodynamics
We propose a structured illumination microscopy method to combine super resolution and optical sectioning in three‐dimensional (3D) samples that allows the use of two‐dimensional (2D) data processing. Indeed, obtaining super‐resolution images of thick samples is a difficult task if low spatial frequencies are present in the in‐focus section of the sample, as these frequencies have to be distinguished from the out‐of‐focus background. A rigorous treatment would require a 3D reconstruction of the whole sample using a 3D point spread function and a 3D stack of structured illumination data. The number of raw images required, 15 per optical section in this case, limits the rate at which high‐resolution images can be obtained. We show that by a succession of two different treatments of structured illumination data we can estimate the contrast of the illumination pattern and remove the out‐of‐focus content from the raw images. After this cleaning step, we can obtain super‐resolution images of optical sections in thick samples using a two‐beam harmonic illumination pattern and a limited number of raw images. This two‐step processing makes it possible to obtain super resolved optical sections in thick samples as fast as if the sample was two‐dimensional.