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Endoscopic video defogging using luminance blending.

Xiongbiao Luo1, Fan Yang1, Hui-Qing Zeng2

  • 1School of Informatics, Xiamen University, Xiamen 361005, People's Republic of China.

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|February 11, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to remove fog and smoke from endoscopic videos during robotic surgery. The technique enhances surgical vision by improving image clarity, leading to better surgical field visualization.

Keywords:
biomedical optical imagingcauterisationclinical endoscopic videosdirect surgical fieldendoscopesendoscopic video defoggingendoscopic video imagesendoscopic video sequencesfoggy endoscopic video processingimage enhancementimage sequencesluminance blending frameworkmedical image processingmedical roboticsrobotic surgerysurgerysurgical operationssurgical visionvideo signal processing

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Area of Science:

  • Medical Imaging
  • Robotic Surgery
  • Computer Vision

Background:

  • Endoscopic video is crucial for robotic surgery visualization.
  • Surgical smoke and fog obscure the surgical field, hindering clear vision.
  • Existing methods struggle to effectively clear endoscopic images.

Purpose of the Study:

  • To develop a novel framework for removing fog and smoke from endoscopic videos.
  • To enhance and maintain clear visualization of the surgical field during robotic procedures.
  • To improve the visual quality of foggy endoscopic images.

Main Methods:

  • A new luminance blending framework was proposed.
  • The framework integrates contrast enhancement with visibility restoration.
  • The method was validated using clinical endoscopic videos from robotic surgery.

Main Results:

  • The proposed method effectively removes fog and smoke from endoscopic video images.
  • Visual quality of defogged images improved significantly (from 0.5088 to 0.6475).
  • The technique offers a promising solution for maintaining clear surgical vision.

Conclusions:

  • The developed luminance blending framework successfully enhances endoscopic video clarity.
  • This method supports improved surgical field visualization in robotic surgery.
  • The findings indicate a significant advancement in processing foggy surgical videos.