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Light Acquisition02:16

Light Acquisition

9.7K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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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...
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Related Experiment Video

Updated: Mar 8, 2026

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

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LIME: Low-Light Image Enhancement via Illumination Map Estimation.

Xiaojie Guo, Yu Li, Haibin Ling

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a simple yet effective low-light image enhancement (LIME) method. LIME improves image visibility and enhances computer vision algorithm performance by refining illumination maps.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Multimedia Systems

    Background:

    • Low-light images suffer from poor visibility, degrading visual quality.
    • This degradation negatively impacts computer vision and multimedia algorithms.
    • Existing enhancement methods may lack efficiency or quality.

    Purpose of the Study:

    • To propose a simple and effective low-light image enhancement (LIME) method.
    • To improve the visual quality of images captured in low-light conditions.
    • To enhance the performance of algorithms reliant on high-quality image inputs.

    Main Methods:

    • Estimating pixel illumination by finding maximum R, G, B values.
    • Refining the initial illumination map using a structure prior.
    • Applying the refined illumination map for image enhancement.

    Main Results:

    • Demonstrated efficacy on challenging low-light images.
    • Achieved superior enhancement quality compared to state-of-the-art methods.
    • Showcased high efficiency in the enhancement process.

    Conclusions:

    • The proposed LIME method is effective for low-light image enhancement.
    • LIME offers improved visual aesthetics and algorithm performance.
    • The method provides a superior balance of quality and efficiency.