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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
Total Internal Reflection Fluorescence Microscopy01:05

Total Internal Reflection Fluorescence Microscopy

Total internal reflection fluorescence microscopy or TIRF is an advanced microscopic technique used to visualize fluorophores in samples close to a solid surface with a higher refractive index, such as a glass coverslip. TIRF only allows fluorophores in proximity to the solid surface to be excited. When light from a medium with a lower refractive index (such as air) hits the glass coverslip at a critical angle, the light undergoes total internal reflection stead of passing through the glass.

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A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
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Bayesian estimation for optimized structured illumination microscopy.

François Orieux1, Eduardo Sepulveda, Vincent Loriette

  • 1Quantitative Image Analysis Unit, Institut Pasteur, CNRS URA 2582, 75724 Paris Cedex 15, France. orieux@pasteur.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian framework for structured illumination microscopy, reducing the number of required low-resolution images for high-resolution image reconstruction. This advance simplifies the process and enhances the accessibility of super-resolution microscopy techniques.

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Area of Science:

  • Biophysics
  • Optical Microscopy
  • Image Reconstruction

Background:

  • Structured illumination microscopy (SIM) offers super-resolution imaging by reconstructing high-resolution (HR) images from multiple low-resolution (LR) images.
  • Classical SIM implementations suffer from drawbacks including extensive image acquisition and manual parameter tuning, limiting widespread adoption.

Purpose of the Study:

  • To develop a novel framework for HR image reconstruction in SIM using a Bayesian inverse problem formulation.
  • To overcome the limitations of classical SIM by reducing image acquisition requirements and automating parameter optimization.

Main Methods:

  • A Bayesian inverse problem approach was employed for HR image reconstruction.
  • The framework enables computation of HR images from a reduced set of LR images without specific modulation constraints.
  • Automatic estimation of optimal reconstruction hyperparameters and uncertainty bounds was incorporated.

Main Results:

  • The new framework successfully reconstructs HR images from a reduced number of LR images.
  • Optimal reconstruction hyperparameters and uncertainty bounds are automatically estimated.
  • Numerical evaluations on simulated data and real microscopy data validate the approach.

Conclusions:

  • The developed Bayesian framework significantly advances structured illumination microscopy.
  • This approach facilitates wider adoption of HR microscopy by simplifying the process and improving efficiency.