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Imaging Biological Samples with Optical Microscopy01:18

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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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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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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.
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Updated: Apr 30, 2026

Lensless Fluorescent Microscopy on a Chip
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Optical eigenmode imaging with a sparse constraint.

Wei Wang, Yan Pu Wang, Yao Wu

    Optics Letters
    |May 3, 2014
    PubMed
    Summary

    Optical eigenmode imaging (OEI) can now produce clearer images by incorporating a sparse constraint. This method retrieves lost information, enhancing image quality and compressibility for diverse applications.

    Area of Science:

    • Optics and Photonics
    • Image Reconstruction
    • Computational Imaging

    Background:

    • Optical eigenmode imaging (OEI) is a nonlocal imaging technique.
    • A key limitation of OEI is the difficulty in guaranteeing the completeness of its eigenmodes, often resulting in image blurring.
    • This challenge impacts the fidelity and resolution of reconstructed images.

    Purpose of the Study:

    • To address the blurring issue in Optical eigenmode imaging (OEI).
    • To enhance the image reconstruction quality and compressibility in OEI.
    • To demonstrate the effectiveness of incorporating a sparse constraint into OEI.

    Main Methods:

    • Implementation of a sparse constraint within the OEI framework.
    • Retrieval of lost information during the image reconstruction process.

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  • Analysis of image correlation and compressibility under the sparse constraint.

    • Main Results:

    • Achieved a correlation close to 1 between the original target and the recovered image.
    • Significantly enhanced the compressibility of the reconstructed images.
    • Demonstrated high-quality image reconstruction across a broad spectrum of object and system parameters.

    Conclusions:

    • The sparse constraint effectively overcomes the eigenmode completeness limitation in OEI.
    • This approach significantly improves image fidelity and reduces blurring.
    • The enhanced OEI method offers robust performance for various imaging scenarios.