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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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Updated: May 21, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

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Published on: August 17, 2011

Leveraging Text-Modulated Semantic Guidance for Low-Light Endoscopic Image Enhancement.

Hong Wang, Zhijian Wu, Haodu Fang

    IEEE Transactions on Medical Imaging
    |May 19, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel text-modulated semantic-aware discriminator (TMSD) to enhance low-light endoscopic images. The TMSD improves visibility and diagnostic accuracy without increasing computational cost.

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    Last Updated: May 21, 2026

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Low light conditions in endoscopic imaging degrade image quality, impacting diagnosis and surgical guidance.
    • Existing methods struggle to effectively enhance visibility, contrast, and reduce noise in endoscopic images.
    • Pretrained models like CLIP show promise for vision tasks, but require adaptation for specific applications like low-light endoscopic image enhancement (LLEIE).

    Purpose of the Study:

    • To develop a novel text-modulated semantic-aware discriminator (TMSD) for improving low-light endoscopic image enhancement (LLEIE).
    • To leverage pretrained CLIP model priors for enhanced semantic understanding in endoscopic imaging.
    • To integrate TMSD into existing enhancement baselines to improve visual restoration without additional inference cost.

    Main Methods:

    • Investigated pretrained CLIP model priors and embedded them into a text-modulated semantic-aware discriminator (TMSD).
    • Developed a prompt learning procedure to obtain text and image semantic priors for normal-light endoscopic imaging.
    • Utilized a text modulator to synergize text and image priors, employing convolutional modulation and cross-attention for semantic guidance integration.

    Main Results:

    • TMSD integration improved perceptual quality in seven representative low-light enhancement baselines across five benchmark datasets.
    • Significant improvements were observed in cross-domain clinical generalization scenarios.
    • Downstream segmentation accuracy was notably enhanced, demonstrating the framework's practical application potential.

    Conclusions:

    • The proposed TMSD effectively enhances low-light endoscopic images by leveraging CLIP priors and adversarial learning.
    • TMSD offers a versatile solution for improving visual quality and diagnostic accuracy in endoscopic imaging.
    • The framework demonstrates broad applicability, successfully adapted to metal artifact reduction tasks with no extra inference cost.