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    A new Style-Guided Network (SGNet) enhances low-light colonoscopy videos by addressing flickering and adapting to different imaging styles. This method improves diagnostic accuracy and surgical procedures in endoscopy.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Poor illumination in colonoscopy hinders disease diagnosis and surgical procedures.
    • Existing low-light video enhancement methods often cause flickering due to frame-by-frame processing and lack adaptability to diverse endoscopic imaging styles.

    Purpose of the Study:

    • To develop a novel method for enhancing low-light colonoscopy videos (LLCVE) that overcomes the limitations of existing techniques.
    • To introduce a Style-Guided Network (SGNet) capable of unpaired low-light video enhancement with temporal smoothness and flexible style transfer.

    Main Methods:

    • Proposed a Style-Guided Network (SGNet) utilizing a CycleGAN-based framework for unpaired low-light to normal-light video conversion.
    • Introduced a Temporal Compensation (TC) module to mitigate flickering by leveraging temporal correlations between adjacent frames.
    • Developed a Style Guidance (SG) module that encodes imaging style from text to guide the enhancement process adaptively.

    Main Results:

    • SGNet demonstrated promising performance in Low-Light Colonoscopy Video Enhancement (LLCVE).
    • The method outperformed state-of-the-art techniques in both quantitative metrics and visual quality.
    • The TC and SG modules effectively addressed flickering and enabled flexible style transfer, respectively.

    Conclusions:

    • SGNet offers an effective solution for enhancing low-light colonoscopy videos, improving visual quality and temporal consistency.
    • The proposed approach enhances the potential for accurate disease diagnosis and improved surgical outcomes in endoscopic procedures.
    • SGNet's style guidance capability makes it adaptable to various endoscopic devices and imaging conditions.