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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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Related Experiment Video

Updated: Jun 10, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Multi-frame super-resolution reconstruction of small moving objects.

Adam W M van Eekeren, Klamer Schutte, Lucas J van Vliet

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 24, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new multiframe super-resolution (SR) method to enhance recognition of small moving objects. The technique accurately reconstructs object boundaries and intensity, even with complex backgrounds.

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    Last Updated: Jun 10, 2026

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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    Published on: February 12, 2014

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    Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

    Published on: February 8, 2014

    Area of Science:

    • Computer Vision
    • Image Processing
    • Signal Processing

    Background:

    • Multiframe super-resolution (SR) for small moving objects is challenging due to mixed boundary pixels and frame-to-frame background variations.
    • Existing methods struggle with accurately reconstructing details of small objects against dynamic backgrounds.

    Purpose of the Study:

    • To develop an advanced multiframe SR reconstruction method for small moving objects.
    • To improve the recognition and high-resolution (HR) description of small objects in complex, real-world scenarios.

    Main Methods:

    • The proposed method models the image acquisition system and accounts for space-time variant foreground and background contributions to mixed pixels.
    • It employs a modified total variation constraint for simultaneous estimation of subpixel precise polygon boundaries and HR intensity.
    • A simple linear motion model is assumed for the moving objects.

    Main Results:

    • The method effectively reconstructs high-resolution details of small moving objects, improving their recognition.
    • Accurate estimation of object boundaries and intensity was achieved even with cluttered and changing backgrounds.
    • Experiments on simulated and real-world data demonstrated excellent performance.

    Conclusions:

    • The developed multiframe SR technique offers a robust solution for reconstructing small moving objects.
    • It significantly enhances object recognition capabilities in challenging imaging conditions.
    • The approach provides a valuable tool for applications requiring precise object detail extraction.