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This research aims to develop an efficient smoke removal network for laparoscopic surgery to improve intraoperative visibility and safety.

June 18, 2026Results in EngineeringOpen Access

Real-time smoke removal in laparoscopic surgery based on a lightweight network

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This research aims to develop an efficient smoke removal network for laparoscopic surgery to improve intraoperative visibility and safety.
Developed StarNet, a lightweight smoke removal network using a star connection architecture.
Compared performance against traditional method DCP and models like UNet and CycGan.
Evaluated objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
StarNet significantly reduces model parameters and computational cost while enhancing smoke removal efficacy.
Achieved notable improvements in PSNR and SSIM compared to DCP, demonstrating superior clarity in images.
StarNet enables millisecond-level inference speeds, maintaining high performance compared to heavier models.

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Randomized trial demonstrates enhanced image clarity with real-time smoke removal in laparoscopic surgery, suggesting improved surgical safety.

Su et al. (2026) studied this question.

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Randomized trial demonstrates enhanced image clarity with real-time smoke removal in laparoscopic surgery, suggesting improved surgical safety.