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  3. Total Variation-based Image Decomposition And Denoising For Microscopy Images.
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  3. Total Variation-based Image Decomposition And Denoising For Microscopy Images.

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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.