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International Journal of Frontiers in Engineering Technology, 2021, 3(10); doi: 10.25236/IJFET.2021.031009.

Recent Advances in Deep Learning-based Smoke Removal Techniques for Laparoscopic Images


Zhengxuan Cao1,2, Zhengyi Zhong1,3, Hanze Wei1, Rongrong Liu1, Qun Wang1

Corresponding Author:
Qun Wang

1Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Science, Suzhou, China

2School of Electrical Engineering, Shandong University, Jinan, China 

3School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China 


The visibility of the operating fields can be severely deteriorated by endoscopic smoke generated during laparoscopic surgery due to laser ablation and cauterization. Clinical studies have shown that removing smoke effects to laparoscopic images from the operating room reduces operating time and makes surgeons more comfortable during the procedure. Desmoking approaches based on deep learning have been found to be effective in the removal of laparoscopic smoke. This research will review several cutting-edge strategies for underlying theory and performance evaluations that have been developed in recent years.


Laparoscopic surgery, Smoke removal, Deep learning, Defogging, Supervised learning, Unsupervised learning

Cite This Paper

Zhengxuan Cao, Zhengyi Zhong, Hanze Wei, Rongrong Liu, Qun Wang. Recent Advances in Deep Learning-based Smoke Removal Techniques for Laparoscopic Images. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 10: 74-77. https://doi.org/10.25236/IJFET.2021.031009.


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