Comparison of image reconstruction methods for structured illumination microscopy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F14%3A10285501" target="_blank" >RIV/00216208:11110/14:10285501 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/14:00221797

Výsledek na webu

<a href="http://dx.doi.org/10.1117/12.2052621" target="_blank" >http://dx.doi.org/10.1117/12.2052621</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2052621" target="_blank" >10.1117/12.2052621</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of image reconstruction methods for structured illumination microscopy

Popis výsledku v původním jazyce

Structured illumination microscopy (SIM) is a recent microscopy technique that enables one to go beyond the diffraction limit using patterned illumination. The high frequency information is encoded through aliasing into the observed image. By acquiring multiple images with different illumination patterns aliased components can be separated and a highresolution image reconstructed. Here we investigate image processing methods that perform the task of high-resolution image reconstruction, namely square-law detection, scaled subtraction, super-resolution SIM (SR-SIM), and Bayesian estimation. The optical sectioning and lateral resolution improvement abilities of these algorithms were tested under various noise level conditions on simulated data and on fluorescence microscopy images of a pollen grain test sample and of a cultured cell stained for the actin cytoskeleton. In order to compare the performance of the algorithms, the following objective criteria were evaluated: Signal to Noise R

Název v anglickém jazyce

Comparison of image reconstruction methods for structured illumination microscopy

Popis výsledku anglicky

Structured illumination microscopy (SIM) is a recent microscopy technique that enables one to go beyond the diffraction limit using patterned illumination. The high frequency information is encoded through aliasing into the observed image. By acquiring multiple images with different illumination patterns aliased components can be separated and a highresolution image reconstructed. Here we investigate image processing methods that perform the task of high-resolution image reconstruction, namely square-law detection, scaled subtraction, super-resolution SIM (SR-SIM), and Bayesian estimation. The optical sectioning and lateral resolution improvement abilities of these algorithms were tested under various noise level conditions on simulated data and on fluorescence microscopy images of a pollen grain test sample and of a cultured cell stained for the actin cytoskeleton. In order to compare the performance of the algorithms, the following objective criteria were evaluated: Signal to Noise R

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Biophotonics: Photonic Solutions for Better Health Care IV

ISBN

978-1-62841-077-8

ISSN

0277-786X

e-ISSN

—

Počet stran výsledku

12

Strana od-do

—

Název nakladatele

SPIE

Místo vydání

Bellingham

Místo konání akce

Brussels, Belgium

Datum konání akce

14. 4. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000343438000081