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Related Concept Videos

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.
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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.
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Related Experiment Video

Updated: Jun 2, 2026

Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection
07:42

Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection

Published on: February 24, 2026

Joint registration and super-resolution with omnidirectional images.

Zafer Arican1, Pascal Frossard

  • 1Signal Processing Laboratory (LTS4), Institute of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. zafer.arican@epfl.ch

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 28, 2011
PubMed
Summary

This study reconstructs high-resolution omnidirectional images from multiple low-resolution inputs, even with registration errors. The spherical Fourier transform (SFT) and total least-squares methods effectively enhance image resolution and quality.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Spherical Imaging

Background:

  • Omnidirectional images are crucial for applications like robotics and virtual reality.
  • Reconstructing high-resolution images from low-resolution sensors is challenging, especially with registration inaccuracies.

Purpose of the Study:

  • To develop a robust method for high-resolution omnidirectional image reconstruction from multiple low-resolution images.
  • To address challenges posed by inexact registration in spherical imaging frameworks.

Main Methods:

  • Utilizing the spherical Fourier transform (SFT) for image reconstruction in a spherical imaging framework.
  • Formulating the joint registration and super-resolution as a total least-squares norm minimization problem in the SFT domain.
  • Employing l(1)-regularized total least-squares solved by interior point methods.

Main Results:

  • Effective reconstruction of high-resolution omnidirectional images demonstrated with synthetic and natural data.
  • Successful handling of significant registration errors in low-resolution images.
  • Improved reconstruction quality and noise reduction through additional regularization.

Conclusions:

  • The proposed SFT-based super-resolution method is effective for omnidirectional images, even with registration inaccuracies.
  • Increasing the number of low-resolution images significantly enhances reconstruction quality.
  • Regularization is vital for robust performance and improved image fidelity.