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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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Related Experiment Video

Updated: Jun 23, 2026

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function
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Bayesian multi-exposure image fusion for robust high dynamic range ptychography.

Shantanu Kodgirwar, Lars Loetgering, Chang Liu

    Optics Express
    |November 14, 2024
    PubMed
    Summary
    This summary is machine-generated.

    High dynamic range imaging using multi-exposure image fusion (MEF) enhances coherent diffractive imaging resolution. A novel Bayesian MEF approach improves phase retrieval, especially in low signal-to-noise ratio conditions.

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    Area of Science:

    • Optics and Imaging
    • Computational Imaging
    • Scientific Instrumentation

    Background:

    • Coherent diffractive imaging (CDI) resolution is often limited by detector dynamic range.
    • Conventional multi-exposure image fusion (MEF) methods struggle with low signal-to-noise ratio (SNR) and illumination variations.
    • Accurate phase retrieval in ptychography requires robust handling of detector limitations.

    Purpose of the Study:

    • To develop an advanced multi-exposure image fusion (MEF) method for high dynamic range (HDR) imaging in coherent diffractive imaging (CDI).
    • To improve phase retrieval accuracy under challenging experimental conditions, including low SNR and illumination fluctuations.
    • To provide a versatile HDR imaging solution applicable beyond CDI.

    Main Methods:

    • Proposed a Bayesian MEF approach utilizing a modified Poisson distribution to model background and saturation.
    • Employed the expectation-maximization (EM) algorithm for inferring model parameters.
    • Validated the method using both synthetic and experimental ptychography data.

    Main Results:

    • The proposed Bayesian MEF method demonstrated superior performance compared to conventional MEF techniques.
    • Achieved enhanced phase retrieval accuracy, particularly for weakly scattering specimens and limited SNR scenarios.
    • The approach effectively accounts for background noise and detector saturation.

    Conclusions:

    • Robust MEF is crucial for advancing ptychography and other CDI techniques.
    • The Bayesian MEF approach offers significant improvements for HDR imaging in challenging experimental setups.
    • The developed algorithm is provided as a Python package for broad accessibility and application.