Bayesian Estimation for Optimized Structured Illumination Microscopy

Structured illumination microscopy is a recent imaging technique that aims at going beyond the classical optical resolution by reconstructing high-resolution (HR) images from low-resolution (LR) images acquired through modulation of the transfer function of the microscope. The classical implementati...

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Bibliographic Details
Published in:IEEE transactions on image processing 2012-02, Vol.21 (2), p.601-614
Main Authors: Orieux, F., Sepulveda, E., Loriette, V., Dubertret, B., Olivo-Marin, J.-C
Format: Article
Language:English
Subjects:
Algorithms
Applications
Applied sciences
Bayes Theorem
Bayesian analysis
Bayesian inversion
Cell Biology
Computer Science
Computer Simulation
Databases, Factual
deconvolution
Detection, estimation, filtering, equalization, prediction
Engineering Sciences
Exact sciences and technology
Frequency modulation
Illumination
Image Processing
Image Processing, Computer-Assisted - methods
image reconstruction
Image resolution
Information, signal and communications theory
Inverse problems
Light
Lighting
Markov chain Monte Carlo (MCMC)
Markov Chains
Mathematical models
Microscopy
Microscopy - methods
Modulation
Modulation, demodulation
Monte Carlo Method
Optical imaging
Optical microscopy
Optimization
Signal and communications theory
Signal and Image Processing
Signal processing
Signal, noise
Signal-To-Noise Ratio
Statistics
structured illumination microscopy (SIM)
Studies
superresolution
Telecommunications and information theory
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Summary:Structured illumination microscopy is a recent imaging technique that aims at going beyond the classical optical resolution by reconstructing high-resolution (HR) images from low-resolution (LR) images acquired through modulation of the transfer function of the microscope. The classical implementation has a number of drawbacks, such as requiring a large number of images to be acquired and parameters to be manually set in an ad-hoc manner that have, until now, hampered its wide dissemination. Here, we present a new framework based on a Bayesian inverse problem formulation approach that enables the computation of one HR image from a reduced number of LR images and has no specific constraints on the modulation. Moreover, it permits to automatically estimate the optimal reconstruction hyperparameters and to compute an uncertainty bound on the estimated values. We demonstrate through numerical evaluations on simulated data and examples on real microscopy data that our approach represents a decisive advance for a wider use of HR microscopy through structured illumination.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2011.2162741