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Single Molecule Fluorescence Microscopy on Planar Supported Bilayers
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Compressed sensing in fluorescence microscopy.

Gianmaria Calisesi1, Alberto Ghezzi2, Daniele Ancora1

  • 1Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy.

Progress in Biophysics and Molecular Biology
|June 21, 2021
PubMed
Summary
This summary is machine-generated.

Compressed sensing (CS) is a powerful signal processing technique that enables high-resolution imaging from limited data. This review explores its principles and applications in fluorescence microscopy and mesoscopy.

Keywords:
Biomedical imagingCompressed sensingComputational imagingFluorescence microscopyInverse problemsOptical imaging

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Area of Science:

  • Optical Imaging
  • Signal Processing

Background:

  • Compressed sensing (CS) addresses ill-posed inverse problems using under-sampled data.
  • CS leverages prior information about object structure for signal reconstruction.
  • Commonly applied in medical imaging (CT, MRI) and data compression.

Purpose of the Study:

  • To review the working principles of compressed sensing.
  • To illustrate CS implementations in optical imaging.
  • To discuss CS applications in fluorescence imaging and mesoscopy.

Main Methods:

  • Exploitation of sparsity constraints in signal processing.
  • Reconstruction of images from under-sampled data.
  • Application of CS algorithms in various microscopy techniques.

Main Results:

  • CS enables high-resolution imaging with reduced data acquisition.
  • Demonstrated value in fluorescence microscopy for super-resolution and light-sheet imaging.
  • Successful applications in mesoscopic imaging for small animal studies.

Conclusions:

  • Compressed sensing is a versatile tool for advanced optical imaging.
  • CS significantly enhances capabilities in fluorescence microscopy and mesoscopy.
  • This review highlights the broad utility and potential of CS in scientific imaging.