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Related Concept Videos

Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
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.
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.
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Phase Contrast and Differential Interference Contrast Microscopy01:26

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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...
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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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Understanding the Changes in Mitochondrial Morphology through Dynamic and Three-dimensional Fluorescence Micrographs
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Total Variation-Based Image Decomposition and Denoising for Microscopy Images.

Marco Corrias1,2, Giada Franceschi3, Michele Riva3

  • 1Faculty of Physics and Center for Computational Materials Science, University of Vienna, 1090 Vienna, Austria.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|May 12, 2026
PubMed
Summary

This study introduces a total variation (TV) workflow for denoising microscopy images, enhancing feature visibility. Huber-ROF and TGV-L1 methods show promise for improving image quality across various microscopy techniques.

Keywords:
AFMSEMSTEMSTMdenoisingimage decompositionmicroscopytotal variation

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Area of Science:

  • Microscopy
  • Image Processing
  • Computational Science

Background:

  • Microscopy images suffer from noise, degrading quality and obscuring features.
  • Increasing image acquisition rates necessitate advanced denoising and restoration techniques.
  • Existing methods may not fully address the complexities of diverse microscopy data.

Purpose of the Study:

  • To develop and evaluate a total variation (TV) based workflow for microscopy image decomposition and denoising.
  • To assess the performance of different TV-based methods (TV-L1, Huber-ROF, TGV-L1) on various microscopy image types.
  • To provide a flexible and widely applicable solution for improving microscopy image quality.

Main Methods:

  • Image restoration via signal component extraction and subtraction.
  • Denoising using total variation (TV) regularization.
  • Comparative evaluation of TV-L1, Huber-ROF, and TGV-L1 algorithms on Atomic Force Microscopy (AFM), Scanning Tunneling Microscopy (STM), and Scanning Electron Microscopy (SEM) images.

Main Results:

  • Huber-ROF demonstrated superior flexibility across different microscopy image types.
  • TGV-L1 proved most effective for direct image denoising.
  • The proposed TV-based workflow showed applicability beyond STM, AFM, and SEM.

Conclusions:

  • The TV-based image processing workflow offers a robust solution for enhancing microscopy image quality.
  • The choice between Huber-ROF and TGV-L1 depends on whether image decomposition or direct denoising is prioritized.
  • Publicly available Python code (AiSurf) facilitates integration into experimental workflows for real-time or post-acquisition image enhancement.